Push model using huggingface_hub.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -1,3 +1,278 @@
----
-license: mit
----
+---
+base_model: akhooli/sbert_ar_nli_500k_ubc_norm
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: ليه فاجعة؟ بس لإنو بجيب سيرة جبران باسيل؟ عم يدق بالزعيم؟
+- text: ديري بالك و خليكي صامدة و قومي افتحي التلفظيون و تابعي انتصارات جيشنا الباسل
+    بسرعة بسرعة
+- text: يا باريس انت حمار ولا بتستحمر
+- text: حطموا أحلام جبران باسيل بالمساهمة بإعادة الإعمار
+- text: وكمان هالمرة أهل الغوطة ضربوا حالهن كيماوي؟
+inference: true
+model-index:
+- name: SetFit with akhooli/sbert_ar_nli_500k_ubc_norm
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8215962441314554
+      name: Accuracy
+---
+
+# SetFit with akhooli/sbert_ar_nli_500k_ubc_norm
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [akhooli/sbert_ar_nli_500k_ubc_norm](https://huggingface.co/akhooli/sbert_ar_nli_500k_ubc_norm) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [akhooli/sbert_ar_nli_500k_ubc_norm](https://huggingface.co/akhooli/sbert_ar_nli_500k_ubc_norm)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 2 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label    | Examples                                                                                                                                                                                                                                                                                       |
+|:---------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| negative | <ul><li>'نشرتكم مقابلة لوزير الخارجية اللبناني جبران باسيل مع سي أن أن تثير الجدل على منصات التواصل ما السبب؟'</li><li>'أود أن أسأل سماحتكم، كيف تحدد نوعية العلاقة أو ما هو تشخيصكم للعلاقة القائمة بينكم وبين الحليف ووزير الخارجية ورئيس...'</li><li>'انت عندك الصيغة الأولية شي'</li></ul> |
+| positive | <ul><li>'بتحط صورة بتقول السوري يضب غراضه ويرحل وبعدين بتقول ما في ضرورة لتغذية الحقد إنتي حمارة ولا عم تستحمري'</li><li>'مش جايي لعندك و على دولة الخلّفك و فيك تفل إذا مش عاجبك'</li><li>'العرب كلهم بدهم حرق وبس'</li></ul>                                                                 |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8216   |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("akhooli/setfit_ar_ubc_hs")
+# Run inference
+preds = model("يا باريس انت حمار ولا بتستحمر")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median  | Max |
+|:-------------|:----|:--------|:----|
+| Word count   | 1   | 12.1725 | 52  |
+
+| Label    | Training Sample Count |
+|:---------|:----------------------|
+| negative | 1978                  |
+| positive | 2800                  |
+
+### Training Hyperparameters
+- batch_size: (32, 32)
+- num_epochs: (1, 1)
+- max_steps: 8000
+- sampling_strategy: undersampling
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- run_name: setfit_hate_25kv8
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0003 | 1    | 0.283         | -               |
+| 0.025  | 100  | 0.2635        | -               |
+| 0.05   | 200  | 0.218         | -               |
+| 0.075  | 300  | 0.1592        | -               |
+| 0.1    | 400  | 0.1118        | -               |
+| 0.125  | 500  | 0.0777        | -               |
+| 0.15   | 600  | 0.0567        | -               |
+| 0.175  | 700  | 0.0394        | -               |
+| 0.2    | 800  | 0.03          | -               |
+| 0.225  | 900  | 0.0212        | -               |
+| 0.25   | 1000 | 0.0205        | -               |
+| 0.275  | 1100 | 0.0172        | -               |
+| 0.3    | 1200 | 0.0142        | -               |
+| 0.325  | 1300 | 0.0098        | -               |
+| 0.35   | 1400 | 0.0097        | -               |
+| 0.375  | 1500 | 0.0064        | -               |
+| 0.4    | 1600 | 0.0044        | -               |
+| 0.425  | 1700 | 0.005         | -               |
+| 0.45   | 1800 | 0.0034        | -               |
+| 0.475  | 1900 | 0.0028        | -               |
+| 0.5    | 2000 | 0.0034        | -               |
+| 0.525  | 2100 | 0.0052        | -               |
+| 0.55   | 2200 | 0.0041        | -               |
+| 0.575  | 2300 | 0.0028        | -               |
+| 0.6    | 2400 | 0.002         | -               |
+| 0.625  | 2500 | 0.0015        | -               |
+| 0.65   | 2600 | 0.0021        | -               |
+| 0.675  | 2700 | 0.0032        | -               |
+| 0.7    | 2800 | 0.0028        | -               |
+| 0.725  | 2900 | 0.0017        | -               |
+| 0.75   | 3000 | 0.0029        | -               |
+| 0.775  | 3100 | 0.0018        | -               |
+| 0.8    | 3200 | 0.0028        | -               |
+| 0.825  | 3300 | 0.0014        | -               |
+| 0.85   | 3400 | 0.002         | -               |
+| 0.875  | 3500 | 0.001         | -               |
+| 0.9    | 3600 | 0.0012        | -               |
+| 0.925  | 3700 | 0.0007        | -               |
+| 0.95   | 3800 | 0.0013        | -               |
+| 0.975  | 3900 | 0.0011        | -               |
+| 1.0    | 4000 | 0.0012        | -               |
+| 1.025  | 4100 | 0.0013        | -               |
+| 1.05   | 4200 | 0.0017        | -               |
+| 1.075  | 4300 | 0.0013        | -               |
+| 1.1    | 4400 | 0.0013        | -               |
+| 1.125  | 4500 | 0.0008        | -               |
+| 1.15   | 4600 | 0.0007        | -               |
+| 1.175  | 4700 | 0.0008        | -               |
+| 1.2    | 4800 | 0.0015        | -               |
+| 1.225  | 4900 | 0.0017        | -               |
+| 1.25   | 5000 | 0.0012        | -               |
+| 1.275  | 5100 | 0.0008        | -               |
+| 1.3    | 5200 | 0.001         | -               |
+| 1.325  | 5300 | 0.0009        | -               |
+| 1.35   | 5400 | 0.0008        | -               |
+| 1.375  | 5500 | 0.0004        | -               |
+| 1.4    | 5600 | 0.0014        | -               |
+| 1.425  | 5700 | 0.001         | -               |
+| 1.45   | 5800 | 0.0013        | -               |
+| 1.475  | 5900 | 0.0009        | -               |
+| 1.5    | 6000 | 0.0           | -               |
+| 1.525  | 6100 | 0.0008        | -               |
+| 1.55   | 6200 | 0.0003        | -               |
+| 1.575  | 6300 | 0.0009        | -               |
+| 1.6    | 6400 | 0.0007        | -               |
+| 1.625  | 6500 | 0.0002        | -               |
+| 1.65   | 6600 | 0.0008        | -               |
+| 1.675  | 6700 | 0.0005        | -               |
+| 1.7    | 6800 | 0.0005        | -               |
+| 1.725  | 6900 | 0.0005        | -               |
+| 1.75   | 7000 | 0.0004        | -               |
+| 1.775  | 7100 | 0.001         | -               |
+| 1.8    | 7200 | 0.0006        | -               |
+| 1.825  | 7300 | 0.0003        | -               |
+| 1.85   | 7400 | 0.0004        | -               |
+| 1.875  | 7500 | 0.0002        | -               |
+| 1.9    | 7600 | 0.0002        | -               |
+| 1.925  | 7700 | 0.0001        | -               |
+| 1.95   | 7800 | 0.0002        | -               |
+| 1.975  | 7900 | 0.0002        | -               |
+| 2.0    | 8000 | 0.0002        | -               |
+
+### Framework Versions
+- Python: 3.10.14
+- SetFit: 1.2.0.dev0
+- Sentence Transformers: 3.2.1
+- Transformers: 4.45.1
+- PyTorch: 2.4.0
+- Datasets: 3.0.1
+- Tokenizers: 0.20.0
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "akhooli/sbert_ar_nli_500k_ubc_norm",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 100000
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.2.1",
+    "transformers": "4.45.1",
+    "pytorch": "2.4.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

@@ -0,0 +1,7 @@
+{
+  "labels": [
+    "negative",
+    "positive"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e42375eed335329c139303f78478df8964c0af125d04a7eee1d5ee7968090c65
+size 651387752

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab5c7f6844a83c48bbcff806d678d4d999d754df34798630ac86cc0080fba6b7
+size 7007

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,64 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}