SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Model Details
Model Description
- Model Type: Sentence Transformer
- Base model: sentence-transformers/all-MiniLM-L6-v2
- Maximum Sequence Length: 256 tokens
- Output Dimensionality: 384 tokens
- Similarity Function: Cosine Similarity
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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})
(2): Normalize()
)
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("overfitting-co/A2P-constrastive-all")
# Run inference
sentences = [
'Khaosan Road',
'Reserved',
'Adventurous',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Binary Classification
- Evaluated with
BinaryClassificationEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.9574 |
cosine_accuracy_threshold | 0.8163 |
cosine_f1 | 0.958 |
cosine_f1_threshold | 0.8131 |
cosine_precision | 0.9682 |
cosine_recall | 0.9481 |
cosine_ap | 0.9909 |
dot_accuracy | 0.9574 |
dot_accuracy_threshold | 0.8163 |
dot_f1 | 0.958 |
dot_f1_threshold | 0.8131 |
dot_precision | 0.9682 |
dot_recall | 0.9481 |
dot_ap | 0.9909 |
manhattan_accuracy | 0.9609 |
manhattan_accuracy_threshold | 9.5648 |
manhattan_f1 | 0.9619 |
manhattan_f1_threshold | 9.5648 |
manhattan_precision | 0.9619 |
manhattan_recall | 0.9619 |
manhattan_ap | 0.9909 |
euclidean_accuracy | 0.9574 |
euclidean_accuracy_threshold | 0.6061 |
euclidean_f1 | 0.958 |
euclidean_f1_threshold | 0.6114 |
euclidean_precision | 0.9682 |
euclidean_recall | 0.9481 |
euclidean_ap | 0.9909 |
max_accuracy | 0.9609 |
max_accuracy_threshold | 9.5648 |
max_f1 | 0.9619 |
max_f1_threshold | 9.5648 |
max_precision | 0.9682 |
max_recall | 0.9619 |
max_ap | 0.9909 |
Binary Classification
- Dataset:
test
- Evaluated with
BinaryClassificationEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.9592 |
cosine_accuracy_threshold | 0.7969 |
cosine_f1 | 0.9591 |
cosine_f1_threshold | 0.7969 |
cosine_precision | 0.9574 |
cosine_recall | 0.9609 |
cosine_ap | 0.9878 |
dot_accuracy | 0.9592 |
dot_accuracy_threshold | 0.7969 |
dot_f1 | 0.9591 |
dot_f1_threshold | 0.7969 |
dot_precision | 0.9574 |
dot_recall | 0.9609 |
dot_ap | 0.9878 |
manhattan_accuracy | 0.9557 |
manhattan_accuracy_threshold | 9.8085 |
manhattan_f1 | 0.9558 |
manhattan_f1_threshold | 9.917 |
manhattan_precision | 0.9507 |
manhattan_recall | 0.9609 |
manhattan_ap | 0.9866 |
euclidean_accuracy | 0.9592 |
euclidean_accuracy_threshold | 0.6373 |
euclidean_f1 | 0.9591 |
euclidean_f1_threshold | 0.6373 |
euclidean_precision | 0.9574 |
euclidean_recall | 0.9609 |
euclidean_ap | 0.9878 |
max_accuracy | 0.9592 |
max_accuracy_threshold | 9.8085 |
max_f1 | 0.9591 |
max_f1_threshold | 9.917 |
max_precision | 0.9574 |
max_recall | 0.9609 |
max_ap | 0.9878 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 4,505 training samples
- Columns:
sentence_0
,sentence_1
, andlabel
- Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label type string string int details - min: 3 tokens
- mean: 6.49 tokens
- max: 24 tokens
- min: 3 tokens
- mean: 3.79 tokens
- max: 8 tokens
- 0: ~52.30%
- 1: ~47.70%
- Samples:
sentence_0 sentence_1 label N Seoul Tower
Laid-back
0
Magere Brug
Romantic
1
Polynesian Cultural Center
Adventurous
1
- Loss:
OnlineContrastiveLoss
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size
: 32per_device_eval_batch_size
: 32num_train_epochs
: 5multi_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: noprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 32per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseeval_use_gather_object
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: round_robin
Training Logs
Epoch | Step | Training Loss | max_ap | test_max_ap |
---|---|---|---|---|
1.0 | 141 | - | 0.6780 | - |
2.0 | 282 | - | 0.7538 | - |
3.0 | 423 | - | 0.8064 | - |
3.5461 | 500 | 6.7404 | - | - |
4.0 | 564 | - | 0.9751 | - |
5.0 | 705 | - | 0.9909 | 0.9878 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.1.0
- Tokenizers: 0.19.1
Citation
BibTeX
Sentence Transformers
@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 = "https://arxiv.org/abs/1908.10084",
}
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Base model
sentence-transformers/all-MiniLM-L6-v2Evaluation results
- Cosine Accuracy on Unknownself-reported0.957
- Cosine Accuracy Threshold on Unknownself-reported0.816
- Cosine F1 on Unknownself-reported0.958
- Cosine F1 Threshold on Unknownself-reported0.813
- Cosine Precision on Unknownself-reported0.968
- Cosine Recall on Unknownself-reported0.948
- Cosine Ap on Unknownself-reported0.991
- Dot Accuracy on Unknownself-reported0.957
- Dot Accuracy Threshold on Unknownself-reported0.816
- Dot F1 on Unknownself-reported0.958