emilios/whisper-md-hu

emilios

whisper

transformers

automatic-speech-recognition

apache-2.0

['hu']

['mozilla-foundation/common_voice_11_0', 'google/fleurs']

['whisper-event', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper medium Hungarian El Greco This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the mozilla-foundation/common_voice_11_0,google/fleurs hu,hu_hu dataset. It achieves the following results on the evaluation set: - Loss: 0.3428 - Wer: 18.6422 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.0621 | 1.05 | 1000 | 0.2690 | 20.5099 | | 0.0174 | 2.1 | 2000 | 0.2705 | 19.2292 | | 0.006 | 3.15 | 3000 | 0.2954 | 18.9890 | | 0.0028 | 4.2 | 4000 | 0.3093 | 18.8023 | | 0.0016 | 5.25 | 5000 | 0.3240 | 18.9653 | | 0.0018 | 6.3 | 6000 | 0.3313 | 18.6451 | | 0.0014 | 7.35 | 7000 | 0.3330 | 18.9446 | | 0.0016 | 8.39 | 8000 | 0.3428 | 18.6422 | | 0.0015 | 9.44 | 9000 | 0.3508 | 18.9564 | | 0.001 | 10.49 | 10000 | 0.3569 | 18.8556 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 2.0.0.dev20221216+cu116 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

048ba60184a4972f12b605ebadcb9810

fathyshalab/all-roberta-large-v1-small_talk-4-16-5

fathyshalab

roberta

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-small_talk-4-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co./sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3566 - Accuracy: 0.3855 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7259 | 1.0 | 1 | 2.5917 | 0.2551 | | 2.217 | 2.0 | 2 | 2.5059 | 0.3275 | | 1.7237 | 3.0 | 3 | 2.4355 | 0.3768 | | 1.4001 | 4.0 | 4 | 2.3837 | 0.3739 | | 1.1937 | 5.0 | 5 | 2.3566 | 0.3855 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

c9475561f73f747809f916eb745a5f4b

izumi-lab/electra-small-paper-japanese-discriminator

izumi-lab

electra

transformers

cc-by-sa-4.0

['ja']

['wikipedia']

[]

# ELECTRA small Japanese discriminator This is a [ELECTRA](https://github.com/google-research/electra) model pretrained on texts in the Japanese language. The codes for the pretraining are available at [retarfi/language-pretraining](https://github.com/retarfi/language-pretraining/tree/v1.0). ## Model architecture The model architecture is the same as ELECTRA small in the [original ELECTRA paper](https://arxiv.org/abs/2003.10555); 12 layers, 256 dimensions of hidden states, and 4 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Japanese version of Wikipedia, using Wikipedia dump file as of June 1, 2021. The corpus file is 2.9GB, consisting of approximately 20M sentences. ## Tokenization The texts are first tokenized by MeCab with IPA dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32768. ## Training The models are trained with the same configuration as ELECTRA small in the [original ELECTRA paper](https://arxiv.org/abs/2003.10555); 128 tokens per instance, 128 instances per batch, and 1M training steps. The size of the generator is 1/4 of the size of the discriminator. ## Citation ``` @article{Suzuki-etal-2023-ipm, title = {Constructing and analyzing domain-specific language model for financial text mining} author = {Masahiro Suzuki and Hiroki Sakaji and Masanori Hirano and Kiyoshi Izumi}, journal = {Information Processing & Management}, volume = {60}, number = {2}, pages = {103194}, year = {2023}, doi = {10.1016/j.ipm.2022.103194} } ``` ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/). ## Acknowledgments This work was supported by JSPS KAKENHI Grant Number JP21K12010.

d70713d25806f33491dc6f35afa6548d

google/vit-large-patch32-224-in21k

google

vit

transformers

feature-extraction

apache-2.0

['imagenet-21k']

['vision']

# Vision Transformer (large-sized model) Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification). By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co./models?search=google/vit) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python from transformers import ViTFeatureExtractor, ViTModel from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k') model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) last_hidden_state = outputs.last_hidden_state ``` Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change. ## Training data The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py). Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). ### Pretraining The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224. ## Evaluation results For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance. ### BibTeX entry and citation info ```bibtex @misc{wu2020visual, title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision}, author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda}, year={2020}, eprint={2006.03677}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ```bibtex @inproceedings{deng2009imagenet, title={Imagenet: A large-scale hierarchical image database}, author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li}, booktitle={2009 IEEE conference on computer vision and pattern recognition}, pages={248--255}, year={2009}, organization={Ieee} } ```

84091ad5428754341e4553cacf13c19f

SetFit/distilbert-base-uncased__sst2__train-8-8

SetFit

distilbert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased__sst2__train-8-8 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6925 - Accuracy: 0.5200 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7061 | 1.0 | 3 | 0.6899 | 0.75 | | 0.6627 | 2.0 | 6 | 0.7026 | 0.25 | | 0.644 | 3.0 | 9 | 0.7158 | 0.25 | | 0.6087 | 4.0 | 12 | 0.7325 | 0.25 | | 0.5602 | 5.0 | 15 | 0.7555 | 0.25 | | 0.5034 | 6.0 | 18 | 0.7725 | 0.25 | | 0.4672 | 7.0 | 21 | 0.7983 | 0.25 | | 0.403 | 8.0 | 24 | 0.8314 | 0.25 | | 0.3571 | 9.0 | 27 | 0.8555 | 0.25 | | 0.2792 | 10.0 | 30 | 0.9065 | 0.25 | | 0.2373 | 11.0 | 33 | 0.9286 | 0.25 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2 - Tokenizers 0.10.3

3d128a710b293768f077c9011f60cbef

Helsinki-NLP/opus-mt-fr-gaa

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-fr-gaa * source languages: fr * target languages: gaa * OPUS readme: [fr-gaa](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-gaa/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-gaa/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-gaa/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-gaa/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.fr.gaa | 27.8 | 0.473 |

afed9b58ae82a983b513301878ff26ad

KarelDO/roberta-base.CEBaB_confounding.observational.sa.5-class.seed_43

KarelDO

roberta

transformers

mit

['en']

['OpenTable']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base.CEBaB_confounding.observational.sa.5-class.seed_43 This model is a fine-tuned version of [roberta-base](https://huggingface.co./roberta-base) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.8001 - Accuracy: 0.6987 - Macro-f1: 0.6805 - Weighted-macro-f1: 0.6922 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 2.5.2 - Tokenizers 0.12.1

1bdb004791cf26de3d6a0111ecd62c03

JeremiahZ/bert-base-uncased-mrpc

JeremiahZ

bert

transformers

text-classification

apache-2.0

['en']

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5572 - Accuracy: 0.8578 - F1: 0.9024 - Combined Score: 0.8801 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | No log | 1.0 | 230 | 0.4111 | 0.8088 | 0.8704 | 0.8396 | | No log | 2.0 | 460 | 0.3762 | 0.8480 | 0.8942 | 0.8711 | | 0.4287 | 3.0 | 690 | 0.5572 | 0.8578 | 0.9024 | 0.8801 | | 0.4287 | 4.0 | 920 | 0.6087 | 0.8554 | 0.8977 | 0.8766 | | 0.1172 | 5.0 | 1150 | 0.6524 | 0.8456 | 0.8901 | 0.8678 | ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

015e27db50493ea3793fa508cf3d2723

k3nneth/finetuning-sentiment-model-3000-samples

k3nneth

distilbert

transformers

text-classification

apache-2.0

['imdb']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3046 - Accuracy: 0.87 - F1: 0.8713 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

cae0bec1c7620c8a11b9a9291ffc0f43

anas-awadalla/bart-base-few-shot-k-128-finetuned-squad-seed-4

anas-awadalla

bart

transformers

question-answering

apache-2.0

['squad']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-few-shot-k-128-finetuned-squad-seed-4 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co./facebook/bart-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

b40162d679d8964e5786eb649f403fd8

GItaf/bert-base-uncased-bert-base-uncased-mc-weight0.25-epoch2

GItaf

bert

transformers

text-generation

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-bert-base-uncased-mc-weight0.25-epoch2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

11f2e10f4179c3321644f7f61a745c3f

InternalMegaT/Brazier_Diffusion

InternalMegaT

text-to-image

creativeml-openrail-m

['en']

['stable-diffusion', 'text-to-image', 'image-to-image']

#MODEL BY InternalMegaT How to use: **_brazier_** "your prompt" **_, by Svetoslav Roerich, generative art, aspect ratio 16:9, fortnite art style, stylized layered shapes, warm color scheme art rendition, an ai generated image, by jake parker_** Training on V1 - 3000 steps, 512x512, v1-5 Base, 13 images Uploaded on 12/9/22 Thanks To Liam Brazier for theses art styles. Examples:-      ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co./spaces/CompVis/stable-diffusion-license)

4670641c94bd4122173365bd91fa05d9

arijitx/wav2vec2-xls-r-300m-bengali

arijitx

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['bn']

['openslr', 'SLR53', 'AI4Bharat/IndicCorp']

['automatic-speech-recognition', 'bn', 'hf-asr-leaderboard', 'openslr_SLR53', 'robust-speech-event']

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR53 - bengali dataset. It achieves the following results on the evaluation set. Without language model : - WER: 0.21726385291857586 - CER: 0.04725010353701041 With 5 gram language model trained on 30M sentences randomly chosen from [AI4Bharat IndicCorp](https://indicnlp.ai4bharat.org/corpora/) dataset : - WER: 0.15322879016421437 - CER: 0.03413696666806267 Note : 5% of a total 10935 samples have been used for evaluation. Evaluation set has 10935 examples which was not part of training training was done on first 95% and eval was done on last 5%. Training was stopped after 180k steps. Output predictions are available under files section. ### Training hyperparameters The following hyperparameters were used during training: - dataset_name="openslr" - model_name_or_path="facebook/wav2vec2-xls-r-300m" - dataset_config_name="SLR53" - output_dir="./wav2vec2-xls-r-300m-bengali" - overwrite_output_dir - num_train_epochs="50" - per_device_train_batch_size="32" - per_device_eval_batch_size="32" - gradient_accumulation_steps="1" - learning_rate="7.5e-5" - warmup_steps="2000" - length_column_name="input_length" - evaluation_strategy="steps" - text_column_name="sentence" - chars_to_ignore , ? . ! \- \; \: \" “ % ‘ ” � — ’ … – - save_steps="2000" - eval_steps="3000" - logging_steps="100" - layerdrop="0.0" - activation_dropout="0.1" - save_total_limit="3" - freeze_feature_encoder - feat_proj_dropout="0.0" - mask_time_prob="0.75" - mask_time_length="10" - mask_feature_prob="0.25" - mask_feature_length="64" - preprocessing_num_workers 32 ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0 Notes - Training and eval code modified from : https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event. - Bengali speech data was not available from common voice or librispeech multilingual datasets, so OpenSLR53 has been used. - Minimum audio duration of 0.5s has been used to filter the training data which excluded may be 10-20 samples. - OpenSLR53 transcripts are *not* part of LM training and LM used to evaluate.

460637fc234bcbb0796671ebcd5886cd

tensorspeech/tts-mb_melgan-kss-ko

tensorspeech

tensorflowtts

text-to-speech

apache-2.0

['ko']

['KSS']

['tensorflowtts', 'audio', 'text-to-speech', 'mel-to-wav']

# Multi-band MelGAN trained on KSS (Korean) This repository provides a pretrained [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) trained on KSS dataset (ko). For a detail of the model, we encourage you to read more about [TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS). ## Install TensorFlowTTS First of all, please install TensorFlowTTS with the following command: ``` pip install TensorFlowTTS ``` ### Converting your Text to Wav ```python import soundfile as sf import numpy as np import tensorflow as tf from tensorflow_tts.inference import AutoProcessor from tensorflow_tts.inference import TFAutoModel processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-kss-ko") tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-kss-ko") mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-kss-ko") text = "신은 우리의 수학 문제에는 관심이 없다. 신은 다만 경험적으로 통합할 뿐이다." input_ids = processor.text_to_sequence(text) # tacotron2 inference (text-to-mel) decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference( input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0), input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32), speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32), ) # melgan inference (mel-to-wav) audio = mb_melgan.inference(mel_outputs)[0, :, 0] # save to file sf.write('./audio.wav', audio, 22050, "PCM_16") ``` #### Referencing Multi-band MelGAN ``` @misc{yang2020multiband, title={Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech}, author={Geng Yang and Shan Yang and Kai Liu and Peng Fang and Wei Chen and Lei Xie}, year={2020}, eprint={2005.05106}, archivePrefix={arXiv}, primaryClass={cs.SD} } ``` #### Referencing TensorFlowTTS ``` @misc{TFTTS, author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata, Trinh Le and Yunchao He}, title = {TensorflowTTS}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}}, } ```

6c4035ee6c1382614de9a1402229653b

tomXBE/bert-finetuned-squad_2

tomXBE

distilbert

transformers

question-answering

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad_2 This model is a fine-tuned version of [tomXBE/distilbert-base-uncased-finetuned-squad](https://huggingface.co./tomXBE/distilbert-base-uncased-finetuned-squad) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

b88491c289b4e5f95b4c4581222bc0ad

gcmsrc/distilbert-base-uncased-finetuned-emotion

gcmsrc

distilbert

transformers

text-classification

apache-2.0

['emotion']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2179 - Accuracy: 0.9245 - F1: 0.9248 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8178 | 1.0 | 250 | 0.3219 | 0.9035 | 0.8996 | | 0.2526 | 2.0 | 500 | 0.2179 | 0.9245 | 0.9248 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

35c8a0b594224be94b670854b7b356d4

SebastianS/distilbert-base-uncased-finetuned-imdb

SebastianS

distilbert

transformers

fill-mask

apache-2.0

['imdb']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0122 - eval_runtime: 27.9861 - eval_samples_per_second: 35.732 - eval_steps_per_second: 0.572 - epoch: 2.13 - step: 334 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

3d58d1d71998e3c696f1888733f26f0c

venetis/distilbert-base-uncased_finetuned_disaster_tweets

venetis

distilbert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_finetuned_disaster_tweets This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4007 - Accuracy: 0.8399 - F1: 0.8384 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4594 | 1.0 | 191 | 0.4059 | 0.8163 | 0.8164 | | 0.3399 | 2.0 | 382 | 0.3905 | 0.8346 | 0.8333 | | 0.2859 | 3.0 | 573 | 0.4007 | 0.8399 | 0.8384 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

17c967052e73d9b0df89f4a2fa871c7e

vumichien/mobilebert-uncased-squad-v2

vumichien

mobilebert

transformers

question-answering

mit

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # tf-mobilebert-uncased-squad-v2 This model is a fine-tuned version of [csarron/mobilebert-uncased-squad-v2](https://huggingface.co./csarron/mobilebert-uncased-squad-v2) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.17.0 - TensorFlow 2.8.0 - Tokenizers 0.11.6

29ca6f1566af31915c4c0cec1a7e478c

Chikashi/t5-small-finetuned-cnndm1

Chikashi

t5

transformers

text2text-generation

apache-2.0

['cnn_dailymail']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-cnndm1 This model is a fine-tuned version of [t5-small](https://huggingface.co./t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.6853 - Rouge1: 24.4246 - Rouge2: 11.6944 - Rougel: 20.1717 - Rougelsum: 23.0424 - Gen Len: 18.9996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.912 | 0.14 | 5000 | 1.7167 | 24.4232 | 11.7049 | 20.1758 | 23.0345 | 18.9997 | | 1.8784 | 0.28 | 10000 | 1.7018 | 24.4009 | 11.6918 | 20.1561 | 23.0073 | 18.9997 | | 1.8628 | 0.42 | 15000 | 1.6934 | 24.385 | 11.683 | 20.1285 | 22.9823 | 18.9997 | | 1.8594 | 0.56 | 20000 | 1.6902 | 24.4407 | 11.6835 | 20.1734 | 23.0369 | 18.9996 | | 1.8537 | 0.7 | 25000 | 1.6864 | 24.3635 | 11.658 | 20.1318 | 22.9782 | 18.9993 | | 1.8505 | 0.84 | 30000 | 1.6856 | 24.4267 | 11.6991 | 20.1629 | 23.0361 | 18.9994 | | 1.8505 | 0.98 | 35000 | 1.6853 | 24.4246 | 11.6944 | 20.1717 | 23.0424 | 18.9996 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

13b7bba082c5507b11d0b67975323d15

pcuenq/coreml-stable-diffusion-2-1-base

pcuenq

text-to-image

other

['stable-diffusion', 'text-to-image', 'core-ml']

# Stable Diffusion v2 Model Card This model was generated by Hugging Face using [Apple’s repository](https://github.com/apple/ml-stable-diffusion) which has [ASCL](https://github.com/apple/ml-stable-diffusion/blob/main/LICENSE.md). This model card focuses on the model associated with the Stable Diffusion v2.1 model, codebase available [here](https://github.com/Stability-AI/stablediffusion). This stable-diffusion-2-1 model is fine-tuned from stable-diffusion-2 (768-v-ema.ckpt) with an additional 55k steps on the same dataset (with punsafe=0.1), and then fine-tuned for another 155k extra steps with punsafe=0.98. These weights here have been converted to Core ML for use on Apple Silicon hardware. There are 4 variants of the Core ML weights: ``` coreml-stable-diffusion-2-base ├── original │ ├── compiled # Swift inference, "original" attention │ └── packages # Python inference, "original" attention └── split_einsum ├── compiled # Swift inference, "split_einsum" attention └── packages # Python inference, "split_einsum" attention ``` Please, refer to https://huggingface.co./blog/diffusers-coreml for details. - Use it with 🧨 [`diffusers`](https://huggingface.co./stabilityai/stable-diffusion-2-base#examples) - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `512-base-ema.ckpt` [here](https://huggingface.co./stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt). ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co./stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)). - **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co./dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section). ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic. **Training Procedure** Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through the OpenCLIP-ViT/H text-encoder. - The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 200000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq. ## Citation @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } *This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co./dalle-mini/dalle-mini).*

7b768279bed0250608e9410cd9d91eb3

Drazcat/whisper-small-es

Drazcat

whisper

transformers

automatic-speech-recognition

apache-2.0

['es']

['Drazcat/Cencosud']

['hf-asr-leaderboard', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Es - GoCloud This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the 30seg dataset. It achieves the following results on the evaluation set: - Loss: 0.0028 - Wer: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 25 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2944 | 5.56 | 50 | 0.1392 | 79.6117 | | 0.08 | 11.11 | 100 | 0.0569 | 46.0472 | | 0.0204 | 16.67 | 150 | 0.0086 | 0.0 | | 0.0028 | 22.22 | 200 | 0.0028 | 0.0 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

f705a626d461edc70ce27b7f7afc31d7

sentence-transformers/distiluse-base-multilingual-cased-v1

sentence-transformers

distilbert

sentence-transformers

sentence-similarity

apache-2.0

['multilingual']

['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']

# sentence-transformers/distiluse-base-multilingual-cased-v1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v1') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/distiluse-base-multilingual-cased-v1) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

6e4503b762b84a2a4e2692ddbcebbdc1

yuhuizhang/finetuned_gpt2-large_sst2_negation0.2

yuhuizhang

gpt2

transformers

text-generation

mit

['sst2']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_gpt2-large_sst2_negation0.2 This model is a fine-tuned version of [gpt2-large](https://huggingface.co./gpt2-large) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.6892 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.431 | 1.0 | 1072 | 3.3426 | | 1.8756 | 2.0 | 2144 | 3.5903 | | 1.6223 | 3.0 | 3216 | 3.6892 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.2 - Tokenizers 0.12.1

9603b4d930836579e429523e1f82eda2

Helsinki-NLP/opus-mt-fi-no

Helsinki-NLP

marian

transformers

translation

apache-2.0

['fi', False]

['translation']

### fin-nor * source group: Finnish * target group: Norwegian * OPUS readme: [fin-nor](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-nor/README.md) * model: transformer-align * source language(s): fin * target language(s): nno nob * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-nor/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-nor/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-nor/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.fin.nor | 23.5 | 0.426 | ### System Info: - hf_name: fin-nor - source_languages: fin - target_languages: nor - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-nor/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['fi', 'no'] - src_constituents: {'fin'} - tgt_constituents: {'nob', 'nno'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/fin-nor/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/fin-nor/opus-2020-06-17.test.txt - src_alpha3: fin - tgt_alpha3: nor - short_pair: fi-no - chrF2_score: 0.426 - bleu: 23.5 - brevity_penalty: 1.0 - ref_len: 14768.0 - src_name: Finnish - tgt_name: Norwegian - train_date: 2020-06-17 - src_alpha2: fi - tgt_alpha2: no - prefer_old: False - long_pair: fin-nor - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

f5982d0dd4f5d39b7382e88c4f849f4a

pig4431/IMDB_DistilBERT_5E

pig4431

distilbert

transformers

text-classification

apache-2.0

['imdb']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # IMDB_DistilBERT_5EE This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2023 - Accuracy: 0.94 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6748 | 0.03 | 50 | 0.5955 | 0.88 | | 0.4404 | 0.06 | 100 | 0.2853 | 0.9 | | 0.3065 | 0.1 | 150 | 0.2208 | 0.9 | | 0.3083 | 0.13 | 200 | 0.2023 | 0.9333 | | 0.2922 | 0.16 | 250 | 0.1530 | 0.94 | | 0.2761 | 0.19 | 300 | 0.2035 | 0.9267 | | 0.2145 | 0.22 | 350 | 0.2450 | 0.9 | | 0.258 | 0.26 | 400 | 0.1680 | 0.9267 | | 0.2702 | 0.29 | 450 | 0.1607 | 0.9333 | | 0.2587 | 0.32 | 500 | 0.1496 | 0.9467 | | 0.2822 | 0.35 | 550 | 0.1405 | 0.9333 | | 0.2538 | 0.38 | 600 | 0.1396 | 0.9467 | | 0.2707 | 0.42 | 650 | 0.1626 | 0.9333 | | 0.2408 | 0.45 | 700 | 0.1623 | 0.9067 | | 0.2531 | 0.48 | 750 | 0.1300 | 0.9467 | | 0.2014 | 0.51 | 800 | 0.1529 | 0.9333 | | 0.2454 | 0.54 | 850 | 0.1365 | 0.94 | | 0.2282 | 0.58 | 900 | 0.1447 | 0.9533 | | 0.2554 | 0.61 | 950 | 0.1321 | 0.9467 | | 0.24 | 0.64 | 1000 | 0.1256 | 0.9467 | | 0.2239 | 0.67 | 1050 | 0.1290 | 0.9467 | | 0.2865 | 0.7 | 1100 | 0.1288 | 0.9667 | | 0.2456 | 0.74 | 1150 | 0.1299 | 0.9533 | | 0.2407 | 0.77 | 1200 | 0.1565 | 0.9267 | | 0.2256 | 0.8 | 1250 | 0.1262 | 0.96 | | 0.238 | 0.83 | 1300 | 0.1599 | 0.9333 | | 0.2151 | 0.86 | 1350 | 0.1252 | 0.9333 | | 0.187 | 0.9 | 1400 | 0.1132 | 0.9467 | | 0.2218 | 0.93 | 1450 | 0.1030 | 0.9533 | | 0.2371 | 0.96 | 1500 | 0.1036 | 0.9467 | | 0.2264 | 0.99 | 1550 | 0.1041 | 0.9467 | | 0.2159 | 1.02 | 1600 | 0.1338 | 0.9267 | | 0.1773 | 1.06 | 1650 | 0.1218 | 0.94 | | 0.1381 | 1.09 | 1700 | 0.1593 | 0.94 | | 0.1582 | 1.12 | 1750 | 0.1445 | 0.9533 | | 0.1921 | 1.15 | 1800 | 0.1355 | 0.94 | | 0.206 | 1.18 | 1850 | 0.1511 | 0.9467 | | 0.1679 | 1.22 | 1900 | 0.1394 | 0.94 | | 0.1691 | 1.25 | 1950 | 0.1403 | 0.9333 | | 0.2301 | 1.28 | 2000 | 0.1169 | 0.9467 | | 0.1764 | 1.31 | 2050 | 0.1507 | 0.9333 | | 0.1772 | 1.34 | 2100 | 0.1148 | 0.96 | | 0.1749 | 1.38 | 2150 | 0.1203 | 0.94 | | 0.1912 | 1.41 | 2200 | 0.1037 | 0.94 | | 0.1614 | 1.44 | 2250 | 0.1006 | 0.9533 | | 0.1975 | 1.47 | 2300 | 0.0985 | 0.9533 | | 0.1843 | 1.5 | 2350 | 0.0922 | 0.9533 | | 0.1764 | 1.54 | 2400 | 0.1259 | 0.9467 | | 0.1855 | 1.57 | 2450 | 0.1243 | 0.96 | | 0.1272 | 1.6 | 2500 | 0.2107 | 0.9267 | | 0.241 | 1.63 | 2550 | 0.1142 | 0.9533 | | 0.1584 | 1.66 | 2600 | 0.1194 | 0.9467 | | 0.1568 | 1.7 | 2650 | 0.1196 | 0.9533 | | 0.1896 | 1.73 | 2700 | 0.1311 | 0.9533 | | 0.143 | 1.76 | 2750 | 0.1140 | 0.9533 | | 0.227 | 1.79 | 2800 | 0.1482 | 0.9333 | | 0.1404 | 1.82 | 2850 | 0.1366 | 0.94 | | 0.1865 | 1.86 | 2900 | 0.1174 | 0.94 | | 0.1659 | 1.89 | 2950 | 0.1189 | 0.94 | | 0.1882 | 1.92 | 3000 | 0.1144 | 0.9467 | | 0.1403 | 1.95 | 3050 | 0.1358 | 0.94 | | 0.2193 | 1.98 | 3100 | 0.1092 | 0.9533 | | 0.1392 | 2.02 | 3150 | 0.1278 | 0.9267 | | 0.1292 | 2.05 | 3200 | 0.1186 | 0.96 | | 0.0939 | 2.08 | 3250 | 0.1183 | 0.94 | | 0.1356 | 2.11 | 3300 | 0.1939 | 0.94 | | 0.1175 | 2.14 | 3350 | 0.1499 | 0.94 | | 0.1285 | 2.18 | 3400 | 0.1538 | 0.94 | | 0.1018 | 2.21 | 3450 | 0.1796 | 0.9333 | | 0.1342 | 2.24 | 3500 | 0.1540 | 0.94 | | 0.17 | 2.27 | 3550 | 0.1261 | 0.94 | | 0.1548 | 2.3 | 3600 | 0.1375 | 0.9267 | | 0.1415 | 2.34 | 3650 | 0.1264 | 0.9333 | | 0.1096 | 2.37 | 3700 | 0.1252 | 0.9333 | | 0.1001 | 2.4 | 3750 | 0.1546 | 0.94 | | 0.0934 | 2.43 | 3800 | 0.1534 | 0.94 | | 0.1287 | 2.46 | 3850 | 0.1735 | 0.9333 | | 0.0872 | 2.5 | 3900 | 0.1475 | 0.9467 | | 0.0994 | 2.53 | 3950 | 0.1735 | 0.9467 | | 0.1558 | 2.56 | 4000 | 0.1585 | 0.9467 | | 0.1517 | 2.59 | 4050 | 0.2021 | 0.9333 | | 0.1246 | 2.62 | 4100 | 0.1594 | 0.9267 | | 0.1228 | 2.66 | 4150 | 0.1338 | 0.9533 | | 0.1064 | 2.69 | 4200 | 0.1421 | 0.9467 | | 0.1466 | 2.72 | 4250 | 0.1383 | 0.9467 | | 0.1243 | 2.75 | 4300 | 0.1604 | 0.9533 | | 0.1434 | 2.78 | 4350 | 0.1736 | 0.9333 | | 0.1127 | 2.82 | 4400 | 0.1909 | 0.9267 | | 0.0908 | 2.85 | 4450 | 0.1958 | 0.9333 | | 0.1134 | 2.88 | 4500 | 0.1596 | 0.94 | | 0.1345 | 2.91 | 4550 | 0.1604 | 0.9533 | | 0.1913 | 2.94 | 4600 | 0.1852 | 0.9267 | | 0.1382 | 2.98 | 4650 | 0.1852 | 0.9333 | | 0.1109 | 3.01 | 4700 | 0.1905 | 0.9333 | | 0.1144 | 3.04 | 4750 | 0.1655 | 0.94 | | 0.074 | 3.07 | 4800 | 0.2034 | 0.9333 | | 0.0926 | 3.1 | 4850 | 0.1929 | 0.94 | | 0.0911 | 3.13 | 4900 | 0.1703 | 0.9333 | | 0.0933 | 3.17 | 4950 | 0.1826 | 0.9333 | | 0.1003 | 3.2 | 5000 | 0.1716 | 0.94 | | 0.0889 | 3.23 | 5050 | 0.1843 | 0.9267 | | 0.0841 | 3.26 | 5100 | 0.1670 | 0.94 | | 0.0918 | 3.29 | 5150 | 0.1595 | 0.9467 | | 0.0795 | 3.33 | 5200 | 0.1504 | 0.96 | | 0.0978 | 3.36 | 5250 | 0.1317 | 0.96 | | 0.1202 | 3.39 | 5300 | 0.1641 | 0.9533 | | 0.0935 | 3.42 | 5350 | 0.1473 | 0.96 | | 0.0673 | 3.45 | 5400 | 0.1684 | 0.9533 | | 0.0729 | 3.49 | 5450 | 0.1414 | 0.9533 | | 0.077 | 3.52 | 5500 | 0.1669 | 0.9533 | | 0.1264 | 3.55 | 5550 | 0.1364 | 0.96 | | 0.1282 | 3.58 | 5600 | 0.1575 | 0.9467 | | 0.0553 | 3.61 | 5650 | 0.1440 | 0.9467 | | 0.0953 | 3.65 | 5700 | 0.1526 | 0.9533 | | 0.0886 | 3.68 | 5750 | 0.1633 | 0.94 | | 0.0901 | 3.71 | 5800 | 0.1704 | 0.9467 | | 0.0986 | 3.74 | 5850 | 0.1674 | 0.94 | | 0.0849 | 3.77 | 5900 | 0.1989 | 0.9333 | | 0.0815 | 3.81 | 5950 | 0.1942 | 0.94 | | 0.0973 | 3.84 | 6000 | 0.1611 | 0.94 | | 0.0599 | 3.87 | 6050 | 0.1807 | 0.9267 | | 0.1068 | 3.9 | 6100 | 0.1966 | 0.94 | | 0.0889 | 3.93 | 6150 | 0.1979 | 0.9333 | | 0.0854 | 3.97 | 6200 | 0.2012 | 0.9333 | | 0.1207 | 4.0 | 6250 | 0.1983 | 0.9333 | | 0.0735 | 4.03 | 6300 | 0.1795 | 0.94 | | 0.1148 | 4.06 | 6350 | 0.1966 | 0.94 | | 0.0725 | 4.09 | 6400 | 0.2290 | 0.94 | | 0.0576 | 4.13 | 6450 | 0.1936 | 0.9333 | | 0.0477 | 4.16 | 6500 | 0.2090 | 0.9333 | | 0.0722 | 4.19 | 6550 | 0.1878 | 0.9333 | | 0.0936 | 4.22 | 6600 | 0.2087 | 0.94 | | 0.0715 | 4.25 | 6650 | 0.2040 | 0.94 | | 0.0586 | 4.29 | 6700 | 0.1862 | 0.9333 | | 0.0548 | 4.32 | 6750 | 0.1801 | 0.9267 | | 0.0527 | 4.35 | 6800 | 0.1912 | 0.9333 | | 0.0813 | 4.38 | 6850 | 0.1941 | 0.9333 | | 0.0531 | 4.41 | 6900 | 0.1932 | 0.9267 | | 0.0606 | 4.45 | 6950 | 0.2195 | 0.94 | | 0.1213 | 4.48 | 7000 | 0.1975 | 0.9333 | | 0.0807 | 4.51 | 7050 | 0.1915 | 0.9333 | | 0.076 | 4.54 | 7100 | 0.1987 | 0.9333 | | 0.0595 | 4.57 | 7150 | 0.2052 | 0.9333 | | 0.0832 | 4.61 | 7200 | 0.2039 | 0.9333 | | 0.0657 | 4.64 | 7250 | 0.2186 | 0.94 | | 0.0684 | 4.67 | 7300 | 0.2063 | 0.94 | | 0.0429 | 4.7 | 7350 | 0.2056 | 0.94 | | 0.0531 | 4.73 | 7400 | 0.2139 | 0.94 | | 0.0556 | 4.77 | 7450 | 0.2153 | 0.94 | | 0.0824 | 4.8 | 7500 | 0.2010 | 0.9333 | | 0.039 | 4.83 | 7550 | 0.2079 | 0.94 | | 0.068 | 4.86 | 7600 | 0.2140 | 0.94 | | 0.065 | 4.89 | 7650 | 0.2108 | 0.94 | | 0.0359 | 4.93 | 7700 | 0.2058 | 0.94 | | 0.0592 | 4.96 | 7750 | 0.2029 | 0.94 | | 0.0793 | 4.99 | 7800 | 0.2023 | 0.94 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

4b33fabf17949e00311f38ce43b256b2

nishantyadav/cls_crossencoder_zeshel

nishantyadav

apache-2.0

[]

This repo contains the cross-encoder model which uses \[cls\]-token based pooling to score a query-item pair. This model is used in the experiments for our EMNLP 2022 paper titled "[Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization](https://arxiv.org/pdf/2210.12579.pdf)". See [paper](https://arxiv.org/pdf/2210.12579.pdf) and/or [code](https://github.com/iesl/anncur) for more details about the model.

08bb62b7d34ed7537d6fa044d37f534d

espnet/simpleoier_chime4_enh_asr_convtasnet_init_noenhloss_wavlm_transformer_init_raw_en_char

espnet

espnet

cc-by-4.0

['en']

['chime4']

['espnet', 'audio', 'speech-enhancement-recognition']

## ESPnet2 EnhS2T model ### `espnet/simpleoier_chime4_enh_asr_convtasnet_init_noenhloss_wavlm_transformer_init_raw_en_char` This model was trained by simpleoier using chime4 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout 2b663318cd1773fb8685b1e03295b6bc6889c283 pip install -e . cd egs2/chime4/enh_asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_chime4_enh_asr_convtasnet_init_noenhloss_wavlm_transformer_init_raw_en_char ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Thu Apr 28 08:15:30 EDT 2022` - python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]` - espnet version: `espnet 202204` - pytorch version: `pytorch 1.8.1` - Git hash: `` - Commit date: `` ## enh_asr_train_enh_asr_convtasnet_init_noenhloss_wavlm_transformer_init_lr1e-4_accum1_adam_specaug_bypass0_raw_en_char ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_beamformit_2mics|1640|27119|98.5|1.2|0.3|0.2|1.7|19.6| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_beamformit_5mics|1640|27119|98.6|1.1|0.3|0.2|1.5|18.7| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track|1640|27119|98.3|1.3|0.4|0.2|1.9|21.8| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_beamformit_2mics|1640|27120|97.9|1.5|0.5|0.2|2.3|25.2| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_beamformit_5mics|1640|27120|98.4|1.2|0.4|0.1|1.7|19.9| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track|1640|27120|97.2|2.1|0.7|0.3|3.1|28.9| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_beamformit_2mics|1320|21409|97.4|2.0|0.6|0.3|2.9|27.3| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_beamformit_5mics|1320|21409|97.8|1.8|0.4|0.2|2.5|24.3| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_isolated_1ch_track|1320|21409|96.7|2.6|0.7|0.4|3.7|31.6| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_beamformit_2mics|1320|21416|96.6|2.5|1.0|0.3|3.7|32.5| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_beamformit_5mics|1320|21416|97.5|1.9|0.7|0.3|2.9|28.9| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track|1320|21416|94.6|3.7|1.6|0.5|5.9|37.3| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_beamformit_2mics|1640|160390|99.5|0.2|0.3|0.2|0.7|19.6| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_beamformit_5mics|1640|160390|99.6|0.1|0.3|0.2|0.6|18.7| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track|1640|160390|99.4|0.2|0.4|0.2|0.8|21.8| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_beamformit_2mics|1640|160400|99.2|0.3|0.5|0.2|1.1|25.2| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_beamformit_5mics|1640|160400|99.5|0.2|0.3|0.1|0.7|19.9| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track|1640|160400|98.8|0.5|0.7|0.3|1.5|28.9| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_beamformit_2mics|1320|126796|98.9|0.4|0.7|0.3|1.4|27.3| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_beamformit_5mics|1320|126796|99.1|0.4|0.5|0.2|1.1|24.3| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_isolated_1ch_track|1320|126796|98.6|0.6|0.8|0.4|1.8|31.7| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_beamformit_2mics|1320|126812|98.2|0.6|1.1|0.4|2.1|32.5| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_beamformit_5mics|1320|126812|98.8|0.4|0.8|0.3|1.5|28.9| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track|1320|126812|97.0|1.2|1.9|0.6|3.7|37.3| ## EnhS2T config <details><summary>expand</summary> ``` config: conf/tuning/train_enh_asr_convtasnet_init_noenhloss_wavlm_transformer_init_lr1e-4_accum1_adam_specaug_bypass0.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/enh_asr_train_enh_asr_convtasnet_init_noenhloss_wavlm_transformer_init_lr1e-4_accum1_adam_specaug_bypass0_raw_en_char ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: true sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 12 patience: 10 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max - - train - loss - min keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 2 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: - ../enh1/exp/enh_train_enh_convtasnet_small_raw/valid.loss.ave_1best.pth:encoder:enh_model.encoder - ../enh1/exp/enh_train_enh_convtasnet_small_raw/valid.loss.ave_1best.pth:separator:enh_model.separator - ../enh1/exp/enh_train_enh_convtasnet_small_raw/valid.loss.ave_1best.pth:decoder:enh_model.decoder - ../asr1/exp/asr_train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k_raw_en_char/valid.acc.ave.pth:frontend:s2t_model.frontend - ../asr1/exp/asr_train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k_raw_en_char/valid.acc.ave.pth:preencoder:s2t_model.preencoder - ../asr1/exp/asr_train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k_raw_en_char/valid.acc.ave.pth:encoder:s2t_model.encoder - ../asr1/exp/asr_train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k_raw_en_char/valid.acc.ave.pth:ctc:s2t_model.ctc - ../asr1/exp/asr_train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k_raw_en_char/valid.acc.ave.pth:decoder:s2t_model.decoder ignore_init_mismatch: false freeze_param: - s2t_model.frontend.upstream num_iters_per_epoch: null batch_size: 12 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/enh_asr_stats_raw_en_char/train/speech_shape - exp/enh_asr_stats_raw_en_char/train/speech_ref1_shape - exp/enh_asr_stats_raw_en_char/train/text_shape.char valid_shape_file: - exp/enh_asr_stats_raw_en_char/valid/speech_shape - exp/enh_asr_stats_raw_en_char/valid/speech_ref1_shape - exp/enh_asr_stats_raw_en_char/valid/text_shape.char batch_type: folded valid_batch_type: null fold_length: - 80000 - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/tr05_multi_noisy_si284/wav.scp - speech - sound - - dump/raw/tr05_multi_noisy_si284/spk1.scp - speech_ref1 - sound - - dump/raw/tr05_multi_noisy_si284/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dt05_multi_isolated_1ch_track/wav.scp - speech - sound - - dump/raw/dt05_multi_isolated_1ch_track/spk1.scp - speech_ref1 - sound - - dump/raw/dt05_multi_isolated_1ch_track/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0001 scheduler: null scheduler_conf: {} token_list: data/en_token_list/char/tokens.txt src_token_list: null init: xavier_uniform input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true enh_criterions: - name: si_snr conf: {} wrapper: fixed_order wrapper_conf: {} enh_model_conf: stft_consistency: false loss_type: mask_mse mask_type: null asr_model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false extract_feats_in_collect_stats: false st_model_conf: stft_consistency: false loss_type: mask_mse mask_type: null subtask_series: - enh - asr model_conf: calc_enh_loss: false bypass_enh_prob: 0.0 use_preprocessor: true token_type: char bpemodel: null src_token_type: bpe src_bpemodel: null non_linguistic_symbols: data/nlsyms.txt cleaner: null g2p: null enh_encoder: conv enh_encoder_conf: channel: 256 kernel_size: 40 stride: 20 enh_separator: tcn enh_separator_conf: num_spk: 1 layer: 4 stack: 2 bottleneck_dim: 256 hidden_dim: 512 kernel: 3 causal: false norm_type: gLN nonlinear: relu enh_decoder: conv enh_decoder_conf: channel: 256 kernel_size: 40 stride: 20 frontend: s3prl frontend_conf: frontend_conf: upstream: wavlm_large download_dir: ./hub multilayer_feature: true fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 100 num_freq_mask: 4 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: utterance_mvn normalize_conf: {} asr_preencoder: linear asr_preencoder_conf: input_size: 1024 output_size: 128 asr_encoder: transformer asr_encoder_conf: output_size: 256 attention_heads: 4 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 attention_dropout_rate: 0.0 input_layer: conv2d2 normalize_before: true asr_postencoder: null asr_postencoder_conf: {} asr_decoder: transformer asr_decoder_conf: input_layer: embed attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.0 self_attention_dropout_rate: 0.0 src_attention_dropout_rate: 0.0 st_preencoder: null st_preencoder_conf: {} st_encoder: rnn st_encoder_conf: {} st_postencoder: null st_postencoder_conf: {} st_decoder: rnn st_decoder_conf: {} st_extra_asr_decoder: rnn st_extra_asr_decoder_conf: {} st_extra_mt_decoder: rnn st_extra_mt_decoder_conf: {} required: - output_dir - token_list version: '202204' distributed: false ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

7c63a6b381aa05b947ba012c6ae9621a

jbreunig/xlm-roberta-base-finetuned-panx-de

jbreunig

xlm-roberta

transformers

token-classification

mit

['xtreme']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co./xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1370 - F1: 0.8625 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.26 | 1.0 | 525 | 0.1565 | 0.8218 | | 0.1276 | 2.0 | 1050 | 0.1409 | 0.8486 | | 0.0817 | 3.0 | 1575 | 0.1370 | 0.8625 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.10.3

45c0c78c58705d301b013ed518f7066e

anas-awadalla/distilroberta-base-task-specific-distilation-on-squad

anas-awadalla

roberta

transformers

question-answering

apache-2.0

['squad']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilroberta-base-task-specific-distilation-on-squad This model is a fine-tuned version of [distilroberta-base](https://huggingface.co./distilroberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

d7cc6c1af862bd8ba74b5caf040cd7b1

csarron/roberta-base-squad-v1

csarron

roberta

transformers

question-answering

mit

['en']

['squad']

['question-answering', 'roberta', 'roberta-base']

## RoBERTa-base fine-tuned on SQuAD v1 This model was fine-tuned from the HuggingFace [RoBERTa](https://arxiv.org/abs/1907.11692) base checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer). This model is case-sensitive: it makes a difference between english and English. ## Details | Dataset | Split | # samples | | -------- | ----- | --------- | | SQuAD1.1 | train | 96.8K | | SQuAD1.1 | eval | 11.8k | ### Fine-tuning - Python: `3.7.5` - Machine specs: `CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz` `Memory: 32 GiB` `GPUs: 2 GeForce GTX 1070, each with 8GiB memory` `GPU driver: 418.87.01, CUDA: 10.1` - script: ```shell # after install https://github.com/huggingface/transformers cd examples/question-answering mkdir -p data wget -O data/train-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json wget -O data/dev-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json python run_energy_squad.py \ --model_type roberta \ --model_name_or_path roberta-base \ --do_train \ --do_eval \ --train_file train-v1.1.json \ --predict_file dev-v1.1.json \ --per_gpu_train_batch_size 12 \ --per_gpu_eval_batch_size 16 \ --learning_rate 3e-5 \ --num_train_epochs 2.0 \ --max_seq_length 320 \ --doc_stride 128 \ --data_dir data \ --output_dir data/roberta-base-squad-v1 2>&1 | tee train-roberta-base-squad-v1.log ``` It took about 2 hours to finish. ### Results **Model size**: `477M` | Metric | # Value | | ------ | --------- | | **EM** | **83.0** | | **F1** | **90.4** | Note that the above results didn't involve any hyperparameter search. ## Example Usage ```python from transformers import pipeline qa_pipeline = pipeline( "question-answering", model="csarron/roberta-base-squad-v1", tokenizer="csarron/roberta-base-squad-v1" ) predictions = qa_pipeline({ 'context': "The game was played on February 7, 2016 at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.", 'question': "What day was the game played on?" }) print(predictions) # output: # {'score': 0.8625259399414062, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'} ``` > Created by [Qingqing Cao](https://awk.ai/) | [GitHub](https://github.com/csarron) | [Twitter](https://twitter.com/sysnlp) > Made with ❤️ in New York.

14e8fcc27a5ed545053ccaadb923abd2

Helsinki-NLP/opus-mt-zh-en

Helsinki-NLP

marian

transformers

translation

cc-by-4.0

['zh', 'en']

['translation']

### zho-eng ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation) - [Citation Information](#citation-information) - [How to Get Started With the Model](#how-to-get-started-with-the-model) ## Model Details - **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation - **Language(s):** - Source Language: Chinese - Target Language: English - **License:** CC-BY-4.0 - **Resources for more information:** - [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Uses #### Direct Use This model can be used for translation and text-to-text generation. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Further details about the dataset for this model can be found in the OPUS readme: [zho-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-eng/README.md) ## Training #### System Information * helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 * transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b * port_machine: brutasse * port_time: 2020-08-21-14:41 * src_multilingual: False * tgt_multilingual: False #### Training Data ##### Preprocessing * pre-processing: normalization + SentencePiece (spm32k,spm32k) * ref_len: 82826.0 * dataset: [opus](https://github.com/Helsinki-NLP/Opus-MT) * download original weights: [opus-2020-07-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.zip) * test set translations: [opus-2020-07-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.test.txt) ## Evaluation #### Results * test set scores: [opus-2020-07-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.eval.txt) * brevity_penalty: 0.948 ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.zho.eng | 36.1 | 0.548 | ## Citation Information ```bibtex @InProceedings{TiedemannThottingal:EAMT2020, author = {J{\"o}rg Tiedemann and Santhosh Thottingal}, title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld}, booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)}, year = {2020}, address = {Lisbon, Portugal} } ``` ## How to Get Started With the Model ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en") ```

3ec52a58e11a0072e5ec5de1a9e888d9

neelan-elucidate-ai/wav2vec2-tcrs

neelan-elucidate-ai

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-tcrs This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co./facebook/wav2vec2-large-lv60) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9550 - Wer: 1.0657 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 13.6613 | 3.38 | 500 | 3.2415 | 1.0 | | 2.9524 | 6.76 | 1000 | 3.0199 | 1.0 | | 2.9425 | 10.14 | 1500 | 3.0673 | 1.0 | | 2.9387 | 13.51 | 2000 | 3.0151 | 1.0 | | 2.9384 | 16.89 | 2500 | 3.0320 | 1.0 | | 2.929 | 20.27 | 3000 | 2.9691 | 1.0 | | 2.9194 | 23.65 | 3500 | 2.9596 | 1.0 | | 2.9079 | 27.03 | 4000 | 2.9279 | 1.0 | | 2.8957 | 30.41 | 4500 | 2.9647 | 1.0 | | 2.8385 | 33.78 | 5000 | 2.8114 | 1.0193 | | 2.6546 | 37.16 | 5500 | 2.6744 | 1.0983 | | 2.5866 | 40.54 | 6000 | 2.6192 | 1.1071 | | 2.5475 | 43.92 | 6500 | 2.5777 | 1.0950 | | 2.5177 | 47.3 | 7000 | 2.5845 | 1.1220 | | 2.482 | 50.68 | 7500 | 2.5730 | 1.1264 | | 2.4343 | 54.05 | 8000 | 2.5722 | 1.0955 | | 2.3754 | 57.43 | 8500 | 2.5781 | 1.1353 | | 2.3055 | 60.81 | 9000 | 2.6177 | 1.0972 | | 2.2446 | 64.19 | 9500 | 2.6351 | 1.1027 | | 2.1625 | 67.57 | 10000 | 2.6924 | 1.0756 | | 2.1078 | 70.95 | 10500 | 2.6817 | 1.0795 | | 2.0366 | 74.32 | 11000 | 2.7629 | 1.0657 | | 1.9899 | 77.7 | 11500 | 2.7972 | 1.0845 | | 1.9309 | 81.08 | 12000 | 2.8450 | 1.0734 | | 1.8861 | 84.46 | 12500 | 2.8703 | 1.0668 | | 1.8437 | 87.84 | 13000 | 2.9308 | 1.0917 | | 1.8192 | 91.22 | 13500 | 2.9298 | 1.0701 | | 1.7952 | 94.59 | 14000 | 2.9488 | 1.0685 | | 1.7745 | 97.97 | 14500 | 2.9550 | 1.0657 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.18.3 - Tokenizers 0.10.3

083b67d4eb21983fa41f50b6403ecb45

anas-awadalla/bert-base-uncased-few-shot-k-64-finetuned-squad-seed-4

anas-awadalla

bert

transformers

question-answering

apache-2.0

['squad']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-few-shot-k-64-finetuned-squad-seed-4 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3

6ca0dc834e39a7313276f3ed8fa8f903

jonatasgrosman/exp_w2v2t_fa_vp-fr_s165

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['fa']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'fa']

# exp_w2v2t_fa_vp-fr_s165 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co./facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co./datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

8c903bff2661e6e0b135851d9e57d8c9

ThatGuyVanquish/mt5-base-finetuned-rabbi-kook-nave-4

ThatGuyVanquish

mt5

transformers

text2text-generation

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-base-finetuned-rabbi-kook-nave-4 This model is a fine-tuned version of [google/mt5-base](https://huggingface.co./google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0 | 1.0 | 1784 | nan | | 0.0 | 2.0 | 3568 | nan | | 0.0 | 3.0 | 5352 | nan | | 0.0 | 4.0 | 7136 | nan | | 0.0 | 5.0 | 8920 | nan | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.11.0

e6c887d58be0c023daca439cba1fc002

sschet/biomedical-ner-all

sschet

distilbert

transformers

token-classification

apache-2.0

['en']

['tner/bc5cdr', 'commanderstrife/jnlpba', 'bc2gm_corpus', 'drAbreu/bc4chemd_ner', 'linnaeus', 'chintagunta85/ncbi_disease']

0.0279399890043426

['Token Classification']

## About the Model An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased - Dataset: Maccrobat https://figshare.com/articles/dataset/MACCROBAT2018/9764942 - Carbon emission: 0.0279399890043426 Kg - Training time: 30.16527 minutes - GPU used : 1 x GeForce RTX 3060 Laptop GPU Checkout the tutorial video for explanation of this model and corresponding python library: https://youtu.be/xpiDPdBpS18 ## Usage The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library. ```python from transformers import pipeline from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all") model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all") pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu pipe("""The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.""") ``` ## Author This model is part of the Research topic "AI in Biomedical field" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at: > https://github.com/dreji18/Bio-Epidemiology-NER

21d0b25d28068dccbee2e11a4e02ff3e

Geotrend/bert-base-en-de-cased

Geotrend

bert

transformers

fill-mask

apache-2.0

['multilingual']

['wikipedia']

[]

# bert-base-en-de-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co./bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co./distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-de-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-de-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact [email protected] for any question, feedback or request.

6cad93fd4e52515edb7d3fe3a86f865f

l3cube-pune/hindi-tweets-bert-v2

l3cube-pune

bert

transformers

fill-mask

cc-by-4.0

['hi']

[]

## HindTweetBERT A HindBERT (l3cube-pune/hindi-bert-v2) model finetuned on Hindi Tweets.<br> More details on the dataset, models, and baseline results can be found in our [paper] (<a href='https://arxiv.org/abs/2210.04267'> link </a>)<br> ``` @article{gokhale2022spread, title={Spread Love Not Hate: Undermining the Importance of Hateful Pre-training for Hate Speech Detection}, author={Gokhale, Omkar and Kane, Aditya and Patankar, Shantanu and Chavan, Tanmay and Joshi, Raviraj}, journal={arXiv preprint arXiv:2210.04267}, year={2022} } ```

1b457a9014efcb374a37cacfd8c694da

Graphcore/lxmert-vqa-uncased

Graphcore

lxmert

transformers

question-answering

apache-2.0

['Graphcore/vqa-lxmert']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Graphcore/lxmert-vqa-uncased Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co./hardware/graphcore). Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project. ## Model description LXMERT is a transformer model for learning vision-and-language cross-modality representations. It has a Transformer model that has three encoders: object relationship encoder, a language encoder, and a cross-modality encoder. It is pretrained via a combination of masked language modelling, visual-language text alignment, ROI-feature regression, masked visual-attribute modelling, masked visual-object modelling, and visual-question answering objectives. It achieves the state-of-the-art results on VQA and GQA. Paper link : [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://arxiv.org/pdf/1908.07490.pdf) ## Intended uses & limitations This model is a fine-tuned version of [unc-nlp/lxmert-base-uncased](https://huggingface.co./unc-nlp/lxmert-base-uncased) on the [Graphcore/vqa-lxmert](https://huggingface.co./datasets/Graphcore/vqa-lxmert) dataset. It achieves the following results on the evaluation set: - Loss: 0.0009 - Accuracy: 0.7242 ## Training and evaluation data - [Graphcore/vqa-lxmert](https://huggingface.co./datasets/Graphcore/vqa-lxmert) dataset ## Training procedure Trained on 16 Graphcore Mk2 IPUs using [optimum-graphcore](https://github.com/huggingface/optimum-graphcore). Command line: ``` python examples/question-answering/run_vqa.py \ --model_name_or_path unc-nlp/lxmert-base-uncased \ --ipu_config_name Graphcore/lxmert-base-ipu \ --dataset_name Graphcore/vqa-lxmert \ --do_train \ --do_eval \ --max_seq_length 512 \ --per_device_train_batch_size 1 \ --num_train_epochs 4 \ --dataloader_num_workers 64 \ --logging_steps 5 \ --learning_rate 5e-5 \ --lr_scheduler_type linear \ --loss_scaling 16384 \ --weight_decay 0.01 \ --warmup_ratio 0.1 \ --output_dir /tmp/vqa/ \ --dataloader_drop_last \ --replace_qa_head \ --pod_type pod16 ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: IPU - total_train_batch_size: 64 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4.0 - training precision: Mixed Precision ### Training results ``` ***** train metrics ***** "epoch": 4.0, "train_loss": 0.0060005393999575125, "train_runtime": 13854.802, "train_samples": 443757, "train_samples_per_second": 128.116, "train_steps_per_second": 2.002 ***** eval metrics ***** "eval_accuracy": 0.7242196202278137, "eval_loss": 0.0008745193481445312, "eval_samples": 214354, ``` ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.0+cpu - Datasets 2.0.0 - Tokenizers 0.11.6

d8e35078e8ee0cc0645dae920da9c20e

Matthijs/mobilevit-small

Matthijs

mobilevit

transformers

image-classification

other

['imagenet-1k']

['vision', 'image-classification']

# MobileViT (small-sized model) MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this repository](https://github.com/apple/ml-cvnets). The license used is [Apple sample code license](https://github.com/apple/ml-cvnets/blob/main/LICENSE). Disclaimer: The team releasing MobileViT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description MobileViT is a light-weight, low latency convolutional neural network that combines MobileNetV2-style layers with a new block that replaces local processing in convolutions with global processing using transformers. As with ViT (Vision Transformer), the image data is converted into flattened patches before it is processed by the transformer layers. Afterwards, however, the patches are "unflattened" back into feature maps. This allows the MobileViT-block to be placed anywhere inside a CNN. MobileViT does not require any positional embeddings. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co./models?search=mobilevit) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import MobileViTFeatureExtractor, MobileViTForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = MobileViTFeatureExtractor.from_pretrained('Matthijs/mobilevit-small') model = MobileViTForImageClassification.from_pretrained('Matthijs/mobilevit-small') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Currently, both the feature extractor and model support PyTorch. ## Training data The MobileViT model was pretrained on [ImageNet-1k](https://huggingface.co./datasets/imagenet-1k), a dataset consisting of 1 million images and 1,000 classes. ## Training procedure ### Preprocessing Training requires only basic data augmentation, i.e. random resized cropping and horizontal flipping. To learn multi-scale representations without requiring fine-tuning, a multi-scale sampler was used during training, with image sizes randomly sampled from: (160, 160), (192, 192), (256, 256), (288, 288), (320, 320). At inference time, images are resized/rescaled to the same resolution (288x288), and center-cropped at 256x256. Pixels are normalized to the range [0, 1]. Images are expected to be in BGR pixel order, not RGB. ### Pretraining The MobileViT networks are trained from scratch for 300 epochs on ImageNet-1k on 8 NVIDIA GPUs with an effective batch size of 1024 and learning rate warmup for 3k steps, followed by cosine annealing. Also used were label smoothing cross-entropy loss and L2 weight decay. Training resolution varies from 160x160 to 320x320, using multi-scale sampling. ## Evaluation results | Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL | |------------------|-------------------------|-------------------------|-----------|----------------------------------------------------| | MobileViT-XXS | 69.0 | 88.9 | 1.3 M | https://huggingface.co./Matthijs/mobilevit-xx-small | | MobileViT-XS | 74.8 | 92.3 | 2.3 M | https://huggingface.co./Matthijs/mobilevit-x-small | | **MobileViT-S** | **78.4** | **94.1** | **5.6 M** | https://huggingface.co./Matthijs/mobilevit-small | ### BibTeX entry and citation info ```bibtex @inproceedings{vision-transformer, title = {MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer}, author = {Sachin Mehta and Mohammad Rastegari}, year = {2022}, URL = {https://arxiv.org/abs/2110.02178} } ```

91fbbc2e6e5447f91edb7186368ec6f3

W4nkel/distilbertBase128KTrain

W4nkel

distilbert

transformers

text-classification

mit

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # W4nkel/distilbertBase128KTrain This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co./dbmdz/distilbert-base-turkish-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7462 - Validation Loss: 0.5115 - Train Accuracy: 0.7675 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.7462 | 0.5115 | 0.7675 | 0 | ### Framework versions - Transformers 4.25.1 - TensorFlow 2.11.0 - Datasets 2.8.0 - Tokenizers 0.13.2

a6c2e7d6b835faa64c075bdbe0f8e761

kompactss/JeBERT_ko_je_v2

kompactss

encoder-decoder

transformers

text2text-generation

afl-3.0

[]

# 🍊 제주 방언 번역 모델 🍊 - 표준어 -> 제주어 - Made by. 구름 자연어처리 과정 3기 3조!! - github link : https://github.com/Goormnlpteam3/JeBERT ## 1. Seq2Seq Transformer Model - encoder : BertConfig - decoder : BertConfig - Tokenizer : WordPiece Tokenizer ## 2. Dataset - Jit Dataset - AI HUB(+아래아 문자)_v2 ## 3. Hyper Parameters - Epoch : 10 epochs(best at 7 epoch) - Random Seed : 42 - Learning Rate : 5e-5 - Warm up Ratio : 0.1 - Batch Size : 32 ## 4. BLEU Score - Jit + AI HUB(+아래아 문자) Dataset : 67.6 --- ### CREDIT - 주형준 : [email protected] - 강가람 : [email protected] - 고광연 : [email protected] - 김수연 : [email protected] - 이원경 : [email protected] - 조성은 : [email protected]

a5b95519c32c5ac5fffe4732cd9b31d8

anas-awadalla/t5-base-few-shot-k-256-finetuned-squad-infilling-seed-4

anas-awadalla

t5

transformers

text2text-generation

apache-2.0

['squad']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-few-shot-k-256-finetuned-squad-infilling-seed-4 This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co./google/t5-v1_1-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 35.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

5dcb7a2d61a3d8cde604553b3150832f

zhiguoxu/chinese-macbert-base-finetuned-ner

zhiguoxu

bert

transformers

token-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # chinese-macbert-base-finetuned-ner This model is a fine-tuned version of [hfl/chinese-macbert-base](https://huggingface.co./hfl/chinese-macbert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2420 - F1: 0.9224 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 57 - eval_batch_size: 57 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.6141 | 1.0 | 1 | 2.6454 | 0.0 | | 2.7076 | 2.0 | 2 | 2.0034 | 0.0 | | 2.0979 | 3.0 | 3 | 1.6276 | 0.0 | | 1.7264 | 4.0 | 4 | 1.3419 | 0.3522 | | 1.4691 | 5.0 | 5 | 1.1239 | 0.4091 | | 1.2504 | 6.0 | 6 | 0.9532 | 0.5514 | | 1.0798 | 7.0 | 7 | 0.8129 | 0.5895 | | 0.9279 | 8.0 | 8 | 0.6987 | 0.625 | | 0.8179 | 9.0 | 9 | 0.6081 | 0.6392 | | 0.7202 | 10.0 | 10 | 0.5346 | 0.6667 | | 0.6377 | 11.0 | 11 | 0.4731 | 0.7451 | | 0.5751 | 12.0 | 12 | 0.4226 | 0.7925 | | 0.5202 | 13.0 | 13 | 0.3804 | 0.7685 | | 0.4733 | 14.0 | 14 | 0.3447 | 0.7928 | | 0.44 | 15.0 | 15 | 0.3145 | 0.8509 | | 0.4047 | 16.0 | 16 | 0.2899 | 0.8918 | | 0.3773 | 17.0 | 17 | 0.2707 | 0.8966 | | 0.353 | 18.0 | 18 | 0.2563 | 0.9052 | | 0.3413 | 19.0 | 19 | 0.2468 | 0.9224 | | 0.3314 | 20.0 | 20 | 0.2420 | 0.9224 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.12.0+cu102 - Datasets 1.18.4 - Tokenizers 0.12.1

2a463960b4873fcfcfd597ff81f9c2f7

Helsinki-NLP/opus-mt-tc-big-en-pt

Helsinki-NLP

marian

transformers

translation

cc-by-4.0

['en', 'pt', 'pt_br']

['translation', 'opus-mt-tc']

# opus-mt-tc-big-en-pt Neural machine translation model for translating from English (en) to Portuguese (pt). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2022-03-13 * source language(s): eng * target language(s): pob por * valid target language labels: >>pob<< >>por<< * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-por/opusTCv20210807+bt_transformer-big_2022-03-13.zip) * more information released models: [OPUS-MT eng-por README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-por/README.md) * more information about the model: [MarianMT](https://huggingface.co./docs/transformers/model_doc/marian) This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>pob<<` ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>por<< Tom tried to stab me.", ">>por<< He has been to Hawaii several times." ] model_name = "pytorch-models/opus-mt-tc-big-en-pt" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # O Tom tentou esfaquear-me. # Ele já esteve no Havaí várias vezes. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-pt") print(pipe(">>por<< Tom tried to stab me.")) # expected output: O Tom tentou esfaquear-me. ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-por/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-por/opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | eng-por | tatoeba-test-v2021-08-07 | 0.69320 | 49.6 | 13222 | 105265 | | eng-por | flores101-devtest | 0.71673 | 50.4 | 1012 | 26519 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 3405783 * port time: Wed Apr 13 17:48:54 EEST 2022 * port machine: LM0-400-22516.local

f45be0cd5669a4b113d710e511bf949e

gokuls/bert-base-uncased-mrpc

gokuls

bert

transformers

text-classification

apache-2.0

['en']

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.3693 - Accuracy: 0.8407 - F1: 0.8825 - Combined Score: 0.8616 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.5716 | 1.0 | 29 | 0.5020 | 0.7475 | 0.8437 | 0.7956 | | 0.3969 | 2.0 | 58 | 0.3693 | 0.8407 | 0.8825 | 0.8616 | | 0.2182 | 3.0 | 87 | 0.5412 | 0.8235 | 0.88 | 0.8518 | | 0.1135 | 4.0 | 116 | 0.5104 | 0.8260 | 0.8748 | 0.8504 | | 0.0772 | 5.0 | 145 | 0.6428 | 0.8186 | 0.8655 | 0.8420 | | 0.049 | 6.0 | 174 | 0.6366 | 0.8260 | 0.8725 | 0.8493 | | 0.0356 | 7.0 | 203 | 0.8414 | 0.8358 | 0.8896 | 0.8627 | | 0.0335 | 8.0 | 232 | 0.8573 | 0.8137 | 0.8676 | 0.8407 | | 0.0234 | 9.0 | 261 | 0.8893 | 0.8309 | 0.8856 | 0.8582 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

8d7a27554db4dfb535b333e658cfded3

transformersbook/distilbert-base-uncased-finetuned-clinc

transformersbook

distilbert

transformers

text-classification

apache-2.0

['clinc_oos']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the clinc_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/08_model-compression.ipynb). It achieves the following results on the evaluation set: - Loss: 0.7773 - Accuracy: 0.9174 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2923 | 1.0 | 318 | 3.2893 | 0.7423 | | 2.6307 | 2.0 | 636 | 1.8837 | 0.8281 | | 1.5483 | 3.0 | 954 | 1.1583 | 0.8968 | | 1.0153 | 4.0 | 1272 | 0.8618 | 0.9094 | | 0.7958 | 5.0 | 1590 | 0.7773 | 0.9174 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1+cu102 - Datasets 1.13.0 - Tokenizers 0.10.3

f347d9bc19ca04737cd515774e8f2231

gcmsrc/xlm-roberta-base-finetuned-panx-fr

gcmsrc

xlm-roberta

transformers

token-classification

mit

['xtreme']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co./xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1388 - F1: 0.9069 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7753 | 1.0 | 96 | 0.3149 | 0.7673 | | 0.3286 | 2.0 | 192 | 0.1819 | 0.8707 | | 0.2197 | 3.0 | 288 | 0.1388 | 0.9069 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

1140ef4f8127d67f70b904f222ee2b96

m-aliabbas/idrak_wav2vec_tr

m-aliabbas

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['common_voice']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # idrak_wav2vec_tr This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

6e4bb4a8c2691c05cfcd139b600ecc59

SetFit/distilbert-base-uncased__sst2__train-32-2

SetFit

distilbert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased__sst2__train-32-2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4805 - Accuracy: 0.7699 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7124 | 1.0 | 13 | 0.6882 | 0.5385 | | 0.6502 | 2.0 | 26 | 0.6715 | 0.5385 | | 0.6001 | 3.0 | 39 | 0.6342 | 0.6154 | | 0.455 | 4.0 | 52 | 0.5713 | 0.7692 | | 0.2605 | 5.0 | 65 | 0.5562 | 0.7692 | | 0.1258 | 6.0 | 78 | 0.6799 | 0.7692 | | 0.0444 | 7.0 | 91 | 0.8096 | 0.7692 | | 0.0175 | 8.0 | 104 | 0.9281 | 0.6923 | | 0.0106 | 9.0 | 117 | 0.9826 | 0.6923 | | 0.0077 | 10.0 | 130 | 1.0254 | 0.7692 | | 0.0056 | 11.0 | 143 | 1.0667 | 0.7692 | | 0.0042 | 12.0 | 156 | 1.1003 | 0.7692 | | 0.0036 | 13.0 | 169 | 1.1299 | 0.7692 | | 0.0034 | 14.0 | 182 | 1.1623 | 0.6923 | | 0.003 | 15.0 | 195 | 1.1938 | 0.6923 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2 - Tokenizers 0.10.3

0d3d33e01df81430bc0ebe65da897672

anton-l/wav2vec2-large-xlsr-53-chuvash

anton-l

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['cv']

['common_voice']

['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']

# Wav2Vec2-Large-XLSR-53-Chuvash Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co./facebook/wav2vec2-large-xlsr-53) on Chuvash using the [Common Voice](https://huggingface.co./datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "cv", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-chuvash") model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-chuvash") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Chuvash test data of Common Voice. ```python import torch import torchaudio import urllib.request import tarfile import pandas as pd from tqdm.auto import tqdm from datasets import load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor # Download the raw data instead of using HF datasets to save disk space data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/cv.tar.gz" filestream = urllib.request.urlopen(data_url) data_file = tarfile.open(fileobj=filestream, mode="r|gz") data_file.extractall() wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-chuvash") model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-chuvash") model.to("cuda") cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/cv/test.tsv", sep='\t') clips_path = "cv-corpus-6.1-2020-12-11/cv/clips/" def clean_sentence(sent): sent = sent.lower() # replace non-alpha characters with space sent = "".join(ch if ch.isalpha() else " " for ch in sent) # remove repeated spaces sent = " ".join(sent.split()) return sent targets = [] preds = [] for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]): row["sentence"] = clean_sentence(row["sentence"]) speech_array, sampling_rate = torchaudio.load(clips_path + row["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) row["speech"] = resampler(speech_array).squeeze().numpy() inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) targets.append(row["sentence"]) preds.append(processor.batch_decode(pred_ids)[0]) print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets))) ``` **Test Result**: 40.01 % ## Training The Common Voice `train` and `validation` datasets were used for training. The script used for training can be found [here](github.com)

7efeceeea52fc8412ced499ef42a9c9f

WALIDALI/asmagalally-with-protogen-v2-2

WALIDALI

diffusers

text-to-image

creativeml-openrail-m

['text-to-image', 'stable-diffusion']

### Asmagalally-with-Protogen-v2.2- Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

1aa2f288e4a2d94761f2a31e558b2849

muhtasham/tiny-mlm-glue-stsb-target-glue-mrpc

muhtasham

bert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tiny-mlm-glue-stsb-target-glue-mrpc This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-stsb](https://huggingface.co./muhtasham/tiny-mlm-glue-stsb) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2364 - Accuracy: 0.7132 - F1: 0.8047 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5901 | 4.35 | 500 | 0.5567 | 0.7108 | 0.8072 | | 0.4581 | 8.7 | 1000 | 0.5798 | 0.7377 | 0.8283 | | 0.3115 | 13.04 | 1500 | 0.6576 | 0.7426 | 0.8247 | | 0.197 | 17.39 | 2000 | 0.7977 | 0.7255 | 0.8152 | | 0.1153 | 21.74 | 2500 | 1.0637 | 0.7059 | 0.7973 | | 0.0843 | 26.09 | 3000 | 1.2364 | 0.7132 | 0.8047 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

e7d94714f8f33eb35efcc8610a09e800

asapp/sew-d-mid-400k

asapp

sew-d

transformers

feature-extraction

apache-2.0

['en']

['librispeech_asr']

['speech']

# SEW-D-mid [SEW-D by ASAPP Research](https://github.com/asappresearch/sew) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc... Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi **Abstract** This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes. The original model can be found under https://github.com/asappresearch/sew#model-checkpoints . # Usage See [this blog](https://huggingface.co./blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWDForCTC`.

3ea3c5d66233dfbfb7aff8575436b206

MarioPenguin/bert-model-english1

MarioPenguin

bert

transformers

text-classification

apache-2.0

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # bert-model-english1 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co./bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0274 - Train Accuracy: 0.9914 - Validation Loss: 0.3493 - Validation Accuracy: 0.9303 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.0366 | 0.9885 | 0.3013 | 0.9299 | 0 | | 0.0261 | 0.9912 | 0.3445 | 0.9351 | 1 | | 0.0274 | 0.9914 | 0.3493 | 0.9303 | 2 | ### Framework versions - Transformers 4.16.2 - TensorFlow 2.7.0 - Datasets 1.18.3 - Tokenizers 0.11.0

0b852fec4a973ed5dc1425d625b8d9e5

anas-awadalla/roberta-base-few-shot-k-16-finetuned-squad-seed-10

anas-awadalla

roberta

transformers

question-answering

mit

['squad']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-few-shot-k-16-finetuned-squad-seed-10 This model is a fine-tuned version of [roberta-base](https://huggingface.co./roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3

43e7b551d18145546b48735148db9da6

scasutt/wav2vec2-base_toy_train_data_augmented

scasutt

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base_toy_train_data_augmented This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co./facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0238 - Wer: 0.6969 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.12 | 1.05 | 250 | 3.3998 | 0.9982 | | 3.0727 | 2.1 | 500 | 3.1261 | 0.9982 | | 1.9729 | 3.15 | 750 | 1.4868 | 0.9464 | | 1.3213 | 4.2 | 1000 | 1.2598 | 0.8833 | | 1.0508 | 5.25 | 1250 | 1.0014 | 0.8102 | | 0.8483 | 6.3 | 1500 | 0.9475 | 0.7944 | | 0.7192 | 7.35 | 1750 | 0.9493 | 0.7686 | | 0.6447 | 8.4 | 2000 | 0.9872 | 0.7573 | | 0.6064 | 9.45 | 2250 | 0.9587 | 0.7447 | | 0.5384 | 10.5 | 2500 | 0.9332 | 0.7320 | | 0.4985 | 11.55 | 2750 | 0.9926 | 0.7315 | | 0.4643 | 12.6 | 3000 | 1.0008 | 0.7292 | | 0.4565 | 13.65 | 3250 | 0.9522 | 0.7171 | | 0.449 | 14.7 | 3500 | 0.9685 | 0.7140 | | 0.4307 | 15.75 | 3750 | 1.0080 | 0.7077 | | 0.4239 | 16.81 | 4000 | 0.9950 | 0.7023 | | 0.389 | 17.86 | 4250 | 1.0260 | 0.7007 | | 0.3471 | 18.91 | 4500 | 1.0012 | 0.6966 | | 0.3276 | 19.96 | 4750 | 1.0238 | 0.6969 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6

cfc6e71bd7ab5e9f7c8b82a44c4c74e2

sd-concepts-library/roblox-avatar

sd-concepts-library

mit

[]

### Roblox avatar on Stable Diffusion why am i spending time making these?, anyways. This is the `<roblox-avatar>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). photos were taken from pinterest. Here is the new concept you will be able to use as an `object`:     

b24717809dc59e098e97bcd19616a555

adityavithaldas/distilbert-base-uncased-finetuned-ner

adityavithaldas

distilbert

transformers

token-classification

apache-2.0

['conll2003']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the conll2003 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.10.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3

7a2173108a35872520c76a54cb3813ec

polejowska/swin-tiny-patch4-window7-224-lcbsi-wbc-new

polejowska

swin

transformers

image-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-lcbsi-wbc-new This model is a fine-tuned version of [polejowska/swin-tiny-patch4-window7-224-lcbsi-wbc](https://huggingface.co./polejowska/swin-tiny-patch4-window7-224-lcbsi-wbc) on the WBC dataset. It achieves the following results on the evaluation set: - Loss: 0.0457 - Accuracy: 0.992 - Precision: 0.9920 - Recall: 0.992 - F1: 0.9920 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002562 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.0936 | 0.98 | 27 | 0.0724 | 0.984 | 0.9841 | 0.984 | 0.9840 | | 0.0276 | 1.98 | 54 | 0.0768 | 0.984 | 0.9841 | 0.984 | 0.9839 | | 0.0133 | 2.98 | 81 | 0.0457 | 0.992 | 0.9920 | 0.992 | 0.9920 | ### Framework versions - Transformers 4.25.1 - Pytorch 2.0.0.dev20230107 - Datasets 2.8.0 - Tokenizers 0.13.2

571c16bb87d562f958279ef3fd7e2997

AkashKhamkar/InSumT510k

AkashKhamkar

t5

transformers

text2text-generation

afl-3.0

[]

--- About : This model can be used for text summarization. The dataset on which it was fine tuned consisted of 10,323 articles. The Data Fields : - "Headline" : title of the article - "articleBody" : the main article content - "source" : the link to the readmore page. The data splits were : - Train : 8258. - Vaildation : 2065. ### How to use along with pipeline ```python from transformers import pipeline from transformers import AutoTokenizer, AutoModelForSeq2Seq tokenizer = AutoTokenizer.from_pretrained("AkashKhamkar/InSumT510k") model = AutoModelForSeq2SeqLM.from_pretrained("AkashKhamkar/InSumT510k") summarizer = pipeline("summarization", model=model, tokenizer=tokenizer) summarizer("Text for summarization...", min_length=5, max_length=50) ``` language: - English library_name: Pytorch tags: - Summarization - T5-base - Conditional Modelling -

f19a50db7e0b912f0f5a488eff5c7e5f

anas-awadalla/roberta-large-data-seed-0

anas-awadalla

roberta

transformers

question-answering

mit

['squad']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-data-seed-0 This model is a fine-tuned version of [roberta-large](https://huggingface.co./roberta-large) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 24 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

fdde17cdd471889cf7d09d07bc5348d2

anas-awadalla/roberta-base-few-shot-k-128-finetuned-squad-seed-42

anas-awadalla

roberta

transformers

question-answering

mit

['squad']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-few-shot-k-128-finetuned-squad-seed-42 This model is a fine-tuned version of [roberta-base](https://huggingface.co./roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results {'exact_match': 39.04446546830653, 'f1': 49.90230650794353} ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

16650d889caab94e4ea52460c9d251e3

lmqg/flan-t5-small-squad-ae

lmqg

t5

transformers

text2text-generation

cc-by-4.0

['en']

['lmqg/qg_squad']

['answer extraction']

# Model Card of `lmqg/flan-t5-small-squad-ae` This model is fine-tuned version of [google/flan-t5-small](https://huggingface.co./google/flan-t5-small) for answer extraction on the [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/flan-t5-small](https://huggingface.co./google/flan-t5-small) - **Language:** en - **Training data:** [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="en", model="lmqg/flan-t5-small-squad-ae") # model prediction answers = model.generate_a("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/flan-t5-small-squad-ae") output = pipe("extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.") ``` ## Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co./lmqg/flan-t5-small-squad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:---------------------------------------------------------------| | AnswerExactMatch | 55.83 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | AnswerF1Score | 68.13 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | BERTScore | 91.1 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | Bleu_1 | 48.25 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | Bleu_2 | 43.39 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | Bleu_3 | 38.64 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | Bleu_4 | 34.6 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | METEOR | 42.59 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | MoverScore | 80.54 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | ROUGE_L | 67.61 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: ['ae'] - model: google/flan-t5-small - max_length: 512 - max_length_output: 32 - epoch: 8 - batch: 64 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 1 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co./lmqg/flan-t5-small-squad-ae/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

224f7ad3a025255cbe91c101491e0314

WillHeld/t5-base-vanilla-cstop_artificial

WillHeld

mt5

transformers

text2text-generation

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-vanilla-cstop_artificial This model is a fine-tuned version of [google/mt5-base](https://huggingface.co./google/mt5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1598 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.2724 | 28.5 | 200 | 0.0776 | | 0.0151 | 57.13 | 400 | 0.1004 | | 0.1727 | 85.63 | 600 | 0.1202 | | 0.0133 | 114.25 | 800 | 0.1005 | | 0.0044 | 142.75 | 1000 | 0.1131 | | 0.0022 | 171.38 | 1200 | 0.1285 | | 0.0018 | 199.88 | 1400 | 0.1349 | | 0.0014 | 228.5 | 1600 | 0.1451 | | 0.003 | 257.13 | 1800 | 0.1215 | | 0.003 | 285.63 | 2000 | 0.1345 | | 0.0012 | 314.25 | 2200 | 0.1520 | | 0.001 | 342.75 | 2400 | 0.1486 | | 0.0008 | 371.38 | 2600 | 0.1559 | | 0.0007 | 399.88 | 2800 | 0.1590 | | 0.0006 | 428.5 | 3000 | 0.1598 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2

d57079ed1326c09958e679b24d89c6ab

muhtasham/tiny-vanilla-target-glue-wnli

muhtasham

bert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tiny-vanilla-target-glue-wnli This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co./google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7580 - Accuracy: 0.0986 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6894 | 25.0 | 500 | 0.7552 | 0.3099 | | 0.6681 | 50.0 | 1000 | 0.9797 | 0.1549 | | 0.6258 | 75.0 | 1500 | 1.3863 | 0.1127 | | 0.5659 | 100.0 | 2000 | 1.7580 | 0.0986 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

88a346a2792f245696171a00f6d98940