| --- |
| base_model: google-bert/bert-base-uncased |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy |
| - cosine_accuracy_threshold |
| - cosine_f1 |
| - cosine_f1_threshold |
| - cosine_precision |
| - cosine_recall |
| - cosine_ap |
| - dot_accuracy |
| - dot_accuracy_threshold |
| - dot_f1 |
| - dot_f1_threshold |
| - dot_precision |
| - dot_recall |
| - dot_ap |
| - manhattan_accuracy |
| - manhattan_accuracy_threshold |
| - manhattan_f1 |
| - manhattan_f1_threshold |
| - manhattan_precision |
| - manhattan_recall |
| - manhattan_ap |
| - euclidean_accuracy |
| - euclidean_accuracy_threshold |
| - euclidean_f1 |
| - euclidean_f1_threshold |
| - euclidean_precision |
| - euclidean_recall |
| - euclidean_ap |
| - max_accuracy |
| - max_accuracy_threshold |
| - max_f1 |
| - max_f1_threshold |
| - max_precision |
| - max_recall |
| - max_ap |
| pipeline_tag: sentence-similarity |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:103663 |
| - loss:MultipleNegativesRankingLoss |
| widget: |
| - source_sentence: How much native Icelandic and advanced Icelandic learners can read |
| and understand Old Norse? |
| sentences: |
| - What are the best answers for "Why should I hire you?"in a cool way? |
| - Are girls shy in expressing their feelings? |
| - If I learn Icelandic can I understand old norse texts? |
| - source_sentence: Where can I get quality assistance for budget conveyancing across |
| the Sydney? |
| sentences: |
| - What are the possible options for India to deal with Uri terror attack? |
| - What is the intended purpose of philosophy? |
| - Where can I get quality assistance in Sydney for any property transaction? |
| - source_sentence: What are some of the best IAS coaching institutions in Mumbai? |
| sentences: |
| - What are best IAS coaching institutes in Mumbai? |
| - Do vampires really exist? |
| - What do most women feel during sex? |
| - source_sentence: Is petroleum engineering still a good major? |
| sentences: |
| - What are some of the best sex stories? |
| - Can I clear CAT in 4.5 months? |
| - What is the future of petroleum engineering graduating in 2020? |
| - source_sentence: How can the drive from Edmonton to Auckland be described, and how |
| do these cities' attractions compare to those in Vancouver? |
| sentences: |
| - How can the drive from Edmonton to Auckland be described, and how does the history |
| of these cities compare and contrast to the history of Vancouver? |
| - What are the best hashtags to use as a photographer on instagram? |
| - Which optional subjects can I choose for the IAS exam? |
| model-index: |
| - name: SentenceTransformer based on google-bert/bert-base-uncased |
| results: |
| - task: |
| type: binary-classification |
| name: Binary Classification |
| dataset: |
| name: Unknown |
| type: unknown |
| metrics: |
| - type: cosine_accuracy |
| value: 0.7643828947012523 |
| name: Cosine Accuracy |
| - type: cosine_accuracy_threshold |
| value: 0.8147265911102295 |
| name: Cosine Accuracy Threshold |
| - type: cosine_f1 |
| value: 0.6959193470955354 |
| name: Cosine F1 |
| - type: cosine_f1_threshold |
| value: 0.7402496337890625 |
| name: Cosine F1 Threshold |
| - type: cosine_precision |
| value: 0.5945532101060921 |
| name: Cosine Precision |
| - type: cosine_recall |
| value: 0.838953622964735 |
| name: Cosine Recall |
| - type: cosine_ap |
| value: 0.7112611713824615 |
| name: Cosine Ap |
| - type: dot_accuracy |
| value: 0.7399583457304374 |
| name: Dot Accuracy |
| - type: dot_accuracy_threshold |
| value: 153.5009765625 |
| name: Dot Accuracy Threshold |
| - type: dot_f1 |
| value: 0.6710917251406536 |
| name: Dot F1 |
| - type: dot_f1_threshold |
| value: 133.23265075683594 |
| name: Dot F1 Threshold |
| - type: dot_precision |
| value: 0.5683387761657477 |
| name: Dot Precision |
| - type: dot_recall |
| value: 0.8191990122694652 |
| name: Dot Recall |
| - type: dot_ap |
| value: 0.6542447011722929 |
| name: Dot Ap |
| - type: manhattan_accuracy |
| value: 0.7665197046333613 |
| name: Manhattan Accuracy |
| - type: manhattan_accuracy_threshold |
| value: 176.4288787841797 |
| name: Manhattan Accuracy Threshold |
| - type: manhattan_f1 |
| value: 0.6972882533068157 |
| name: Manhattan F1 |
| - type: manhattan_f1_threshold |
| value: 218.96762084960938 |
| name: Manhattan F1 Threshold |
| - type: manhattan_precision |
| value: 0.590020301314243 |
| name: Manhattan Precision |
| - type: manhattan_recall |
| value: 0.8522262520256193 |
| name: Manhattan Recall |
| - type: manhattan_ap |
| value: 0.7109056366977289 |
| name: Manhattan Ap |
| - type: euclidean_accuracy |
| value: 0.7665197046333613 |
| name: Euclidean Accuracy |
| - type: euclidean_accuracy_threshold |
| value: 8.092199325561523 |
| name: Euclidean Accuracy Threshold |
| - type: euclidean_f1 |
| value: 0.6970045347129081 |
| name: Euclidean F1 |
| - type: euclidean_f1_threshold |
| value: 9.794208526611328 |
| name: Euclidean F1 Threshold |
| - type: euclidean_precision |
| value: 0.5945518932171071 |
| name: Euclidean Precision |
| - type: euclidean_recall |
| value: 0.8421174473338993 |
| name: Euclidean Recall |
| - type: euclidean_ap |
| value: 0.7109417385930392 |
| name: Euclidean Ap |
| - type: max_accuracy |
| value: 0.7665197046333613 |
| name: Max Accuracy |
| - type: max_accuracy_threshold |
| value: 176.4288787841797 |
| name: Max Accuracy Threshold |
| - type: max_f1 |
| value: 0.6972882533068157 |
| name: Max F1 |
| - type: max_f1_threshold |
| value: 218.96762084960938 |
| name: Max F1 Threshold |
| - type: max_precision |
| value: 0.5945532101060921 |
| name: Max Precision |
| - type: max_recall |
| value: 0.8522262520256193 |
| name: Max Recall |
| - type: max_ap |
| value: 0.7112611713824615 |
| name: Max Ap |
| --- |
| |
| # SentenceTransformer based on google-bert/bert-base-uncased |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased). It maps sentences & paragraphs to a 768-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:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 768 tokens |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel |
| (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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
|
|
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("gavinqiangli/my-awesome-bi-encoder") |
| # Run inference |
| sentences = [ |
| "How can the drive from Edmonton to Auckland be described, and how do these cities' attractions compare to those in Vancouver?", |
| 'How can the drive from Edmonton to Auckland be described, and how does the history of these cities compare and contrast to the history of Vancouver?', |
| 'Which optional subjects can I choose for the IAS exam?', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 768] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities.shape) |
| # [3, 3] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Binary Classification |
|
|
| * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
|
|
| | Metric | Value | |
| |:-----------------------------|:-----------| |
| | cosine_accuracy | 0.7644 | |
| | cosine_accuracy_threshold | 0.8147 | |
| | cosine_f1 | 0.6959 | |
| | cosine_f1_threshold | 0.7402 | |
| | cosine_precision | 0.5946 | |
| | cosine_recall | 0.839 | |
| | cosine_ap | 0.7113 | |
| | dot_accuracy | 0.74 | |
| | dot_accuracy_threshold | 153.501 | |
| | dot_f1 | 0.6711 | |
| | dot_f1_threshold | 133.2327 | |
| | dot_precision | 0.5683 | |
| | dot_recall | 0.8192 | |
| | dot_ap | 0.6542 | |
| | manhattan_accuracy | 0.7665 | |
| | manhattan_accuracy_threshold | 176.4289 | |
| | manhattan_f1 | 0.6973 | |
| | manhattan_f1_threshold | 218.9676 | |
| | manhattan_precision | 0.59 | |
| | manhattan_recall | 0.8522 | |
| | manhattan_ap | 0.7109 | |
| | euclidean_accuracy | 0.7665 | |
| | euclidean_accuracy_threshold | 8.0922 | |
| | euclidean_f1 | 0.697 | |
| | euclidean_f1_threshold | 9.7942 | |
| | euclidean_precision | 0.5946 | |
| | euclidean_recall | 0.8421 | |
| | euclidean_ap | 0.7109 | |
| | max_accuracy | 0.7665 | |
| | max_accuracy_threshold | 176.4289 | |
| | max_f1 | 0.6973 | |
| | max_f1_threshold | 218.9676 | |
| | max_precision | 0.5946 | |
| | max_recall | 0.8522 | |
| | **max_ap** | **0.7113** | |
| |
| <!-- |
| ## 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 Dataset |
| |
| #### Unnamed Dataset |
| |
| |
| * Size: 103,663 training samples |
| * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence_0 | sentence_1 | label | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 13.82 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.87 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>0: ~4.80%</li><li>1: ~95.20%</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | label | |
| |:-------------------------------------------------------------------------------------|:---------------------------------------------------------|:---------------| |
| | <code>Are Jewish people the most intelligent in the universe?</code> | <code>Why are Jewish people so intelligent?</code> | <code>1</code> | |
| | <code>How do I become a good lawyer? What are the qualities of a good lawyer?</code> | <code>How can someone become a successful lawyer?</code> | <code>1</code> | |
| | <code>Why is China going to the Moon?</code> | <code>What does China want with the moon?</code> | <code>1</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim" |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `num_train_epochs`: 1 |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: steps |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `per_gpu_train_batch_size`: None |
| - `per_gpu_eval_batch_size`: None |
| - `gradient_accumulation_steps`: 1 |
| - `eval_accumulation_steps`: None |
| - `torch_empty_cache_steps`: None |
| - `learning_rate`: 5e-05 |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1 |
| - `num_train_epochs`: 1 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: {} |
| - `warmup_ratio`: 0.0 |
| - `warmup_steps`: 0 |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `save_safetensors`: True |
| - `save_on_each_node`: False |
| - `save_only_model`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `no_cuda`: False |
| - `use_cpu`: False |
| - `use_mps_device`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `jit_mode_eval`: False |
| - `use_ipex`: False |
| - `bf16`: False |
| - `fp16`: False |
| - `fp16_opt_level`: O1 |
| - `half_precision_backend`: auto |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `local_rank`: 0 |
| - `ddp_backend`: None |
| - `tpu_num_cores`: None |
| - `tpu_metrics_debug`: False |
| - `debug`: [] |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 0 |
| - `dataloader_prefetch_factor`: None |
| - `past_index`: -1 |
| - `disable_tqdm`: False |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `load_best_model_at_end`: False |
| - `ignore_data_skip`: False |
| - `fsdp`: [] |
| - `fsdp_min_num_params`: 0 |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `fsdp_transformer_layer_cls_to_wrap`: None |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch |
| - `optim_args`: None |
| - `adafactor`: False |
| - `group_by_length`: False |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `skip_memory_metrics`: True |
| - `use_legacy_prediction_loop`: False |
| - `push_to_hub`: False |
| - `resume_from_checkpoint`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_private_repo`: False |
| - `hub_always_push`: False |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `include_inputs_for_metrics`: False |
| - `eval_do_concat_batches`: True |
| - `fp16_backend`: auto |
| - `push_to_hub_model_id`: None |
| - `push_to_hub_organization`: None |
| - `mp_parameters`: |
| - `auto_find_batch_size`: False |
| - `full_determinism`: False |
| - `torchdynamo`: None |
| - `ray_scope`: last |
| - `ddp_timeout`: 1800 |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `dispatch_batches`: None |
| - `split_batches`: None |
| - `include_tokens_per_second`: False |
| - `include_num_input_tokens_seen`: False |
| - `neftune_noise_alpha`: None |
| - `optim_target_modules`: None |
| - `batch_eval_metrics`: False |
| - `eval_on_start`: False |
| - `eval_use_gather_object`: False |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | max_ap | |
| |:------:|:----:|:-------------:|:------:| |
| | 0.0772 | 500 | 0.0796 | - | |
| | 0.1543 | 1000 | 0.0205 | 0.6878 | |
| | 0.2315 | 1500 | 0.0197 | - | |
| | 0.3087 | 2000 | 0.0201 | 0.6864 | |
| | 0.3859 | 2500 | 0.0185 | - | |
| | 0.4630 | 3000 | 0.0161 | 0.6933 | |
| | 0.5402 | 3500 | 0.0163 | - | |
| | 0.6174 | 4000 | 0.0172 | 0.7089 | |
| | 0.6946 | 4500 | 0.0172 | - | |
| | 0.7717 | 5000 | 0.0143 | 0.7072 | |
| | 0.8489 | 5500 | 0.0129 | - | |
| | 0.9261 | 6000 | 0.0124 | 0.7112 | |
| | 1.0 | 6479 | - | 0.7113 | |
| |
| |
| ### 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 |
| ```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 = "https://arxiv.org/abs/1908.10084", |
| } |
| ``` |
| |
| #### MultipleNegativesRankingLoss |
| ```bibtex |
| @misc{henderson2017efficient, |
| title={Efficient Natural Language Response Suggestion for Smart Reply}, |
| author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
| year={2017}, |
| eprint={1705.00652}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| } |
| ``` |
| |
| <!-- |
| ## 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.* |
| --> |
