| --- |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| datasets: [] |
| language: [] |
| library_name: sentence-transformers |
| metrics: |
| - pearson_cosine |
| - spearman_cosine |
| - pearson_manhattan |
| - spearman_manhattan |
| - pearson_euclidean |
| - spearman_euclidean |
| - pearson_dot |
| - spearman_dot |
| - pearson_max |
| - spearman_max |
| pipeline_tag: sentence-similarity |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:724 |
| - loss:CoSENTLoss |
| widget: |
| - source_sentence: Financials |
| sentences: |
| - What is the financial performance of ABC? |
| - What companies operate in the same space as ABC? |
| - What standards are used to evaluate the industry? |
| - source_sentence: Research |
| sentences: |
| - What recent studies have been conducted on ABC? |
| - What are the key factors considered in rating ABC? |
| - How is the rating framework applied to the sector? |
| - source_sentence: Criteria |
| sentences: |
| - What are the projected economic impacts of inflation on the technology industry? |
| - What is the process for assessing the creditworthiness of ABC? |
| - What are the primary ESG challenges faced by ABC? |
| - source_sentence: Financials |
| sentences: |
| - Can you list the strengths and weaknesses of ABC? |
| - What is understood by the term sovereign risk? |
| - Can you provide the financial history of ABC? |
| - source_sentence: Research |
| sentences: |
| - What macroeconomic trends are influencing the credit ratings of the automotive |
| industry? |
| - Who are the main rivals of ABC? |
| - Can you provide the latest research insights on ABC? |
| model-index: |
| - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts dev |
| type: sts-dev |
| metrics: |
| - type: pearson_cosine |
| value: .nan |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: .nan |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: .nan |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: .nan |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: .nan |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: .nan |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: .nan |
| name: Pearson Dot |
| - type: spearman_dot |
| value: .nan |
| name: Spearman Dot |
| - type: pearson_max |
| value: .nan |
| name: Pearson Max |
| - type: spearman_max |
| value: .nan |
| name: Spearman Max |
| --- |
| |
| # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/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](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 384 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': 512, '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}) |
| ) |
| ``` |
|
|
| ## 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("ManishThota/QueryRouter") |
| # Run inference |
| sentences = [ |
| 'Research', |
| 'Can you provide the latest research insights on ABC?', |
| 'Who are the main rivals of ABC?', |
| ] |
| 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] |
| ``` |
|
|
| <!-- |
| ### 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 |
|
|
| #### Semantic Similarity |
| * Dataset: `sts-dev` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:--------| |
| | pearson_cosine | nan | |
| | **spearman_cosine** | **nan** | |
| | pearson_manhattan | nan | |
| | spearman_manhattan | nan | |
| | pearson_euclidean | nan | |
| | spearman_euclidean | nan | |
| | pearson_dot | nan | |
| | spearman_dot | nan | |
| | pearson_max | nan | |
| | spearman_max | nan | |
| |
| <!-- |
| ## 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: 724 training samples |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence1 | sentence2 | score | |
| |:--------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 3.27 tokens</li><li>max: 4 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 14.23 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | score | |
| |:--------------------|:-------------------------------------------------|:-----------------| |
| | <code>Rating</code> | <code>What rating does XYZ have?</code> | <code>1.0</code> | |
| | <code>Rating</code> | <code>Can you provide the rating for XYZ?</code> | <code>1.0</code> | |
| | <code>Rating</code> | <code>How is XYZ rated?</code> | <code>1.0</code> | |
| * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "pairwise_cos_sim" |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### Unnamed Dataset |
| |
| |
| * Size: 60 evaluation samples |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence1 | sentence2 | score | |
| |:--------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 3.25 tokens</li><li>max: 4 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 12.48 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | score | |
| |:--------------------|:-------------------------------------------------|:-----------------| |
| | <code>Rating</code> | <code>What is the current rating of ABC?</code> | <code>1.0</code> | |
| | <code>Rating</code> | <code>Can you tell me the rating for ABC?</code> | <code>1.0</code> | |
| | <code>Rating</code> | <code>What rating has ABC been assigned?</code> | <code>1.0</code> | |
| * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "pairwise_cos_sim" |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `eval_strategy`: steps |
| - `learning_rate`: 2e-05 |
| - `num_train_epochs`: 10 |
| - `warmup_ratio`: 0.1 |
| - `save_only_model`: True |
| - `seed`: 33 |
| - `fp16`: True |
| - `load_best_model_at_end`: True |
|
|
| #### 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`: 8 |
| - `per_device_eval_batch_size`: 8 |
| - `per_gpu_train_batch_size`: None |
| - `per_gpu_eval_batch_size`: None |
| - `gradient_accumulation_steps`: 1 |
| - `eval_accumulation_steps`: None |
| - `learning_rate`: 2e-05 |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1.0 |
| - `num_train_epochs`: 10 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: {} |
| - `warmup_ratio`: 0.1 |
| - `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`: True |
| - `restore_callback_states_from_checkpoint`: False |
| - `no_cuda`: False |
| - `use_cpu`: False |
| - `use_mps_device`: False |
| - `seed`: 33 |
| - `data_seed`: None |
| - `jit_mode_eval`: False |
| - `use_ipex`: False |
| - `bf16`: False |
| - `fp16`: True |
| - `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`: True |
| - `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 |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
|
|
| </details> |
|
|
| ### Training Logs |
| <details><summary>Click to expand</summary> |
|
|
| | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | |
| |:----------:|:-------:|:-------------:|:-------:|:-----------------------:| |
| | 0.0220 | 2 | - | 0.0 | nan | |
| | 0.0440 | 4 | - | 0.0 | nan | |
| | 0.0659 | 6 | - | 0.0 | nan | |
| | 0.0879 | 8 | - | 0.0 | nan | |
| | 0.1099 | 10 | - | 0.0 | nan | |
| | 0.1319 | 12 | - | 0.0 | nan | |
| | 0.1538 | 14 | - | 0.0 | nan | |
| | 0.1758 | 16 | - | 0.0 | nan | |
| | 0.1978 | 18 | - | 0.0 | nan | |
| | 0.2198 | 20 | - | 0.0 | nan | |
| | 0.2418 | 22 | - | 0.0 | nan | |
| | 0.2637 | 24 | - | 0.0 | nan | |
| | 0.2857 | 26 | - | 0.0 | nan | |
| | 0.3077 | 28 | - | 0.0 | nan | |
| | 0.3297 | 30 | - | 0.0 | nan | |
| | 0.3516 | 32 | - | 0.0 | nan | |
| | 0.3736 | 34 | - | 0.0 | nan | |
| | 0.3956 | 36 | - | 0.0 | nan | |
| | 0.4176 | 38 | - | 0.0 | nan | |
| | 0.4396 | 40 | - | 0.0 | nan | |
| | 0.4615 | 42 | - | 0.0 | nan | |
| | 0.4835 | 44 | - | 0.0 | nan | |
| | 0.5055 | 46 | - | 0.0 | nan | |
| | 0.5275 | 48 | - | 0.0 | nan | |
| | 0.5495 | 50 | - | 0.0 | nan | |
| | 0.5714 | 52 | - | 0.0 | nan | |
| | 0.5934 | 54 | - | 0.0 | nan | |
| | 0.6154 | 56 | - | 0.0 | nan | |
| | 0.6374 | 58 | - | 0.0 | nan | |
| | 0.6593 | 60 | - | 0.0 | nan | |
| | 0.6813 | 62 | - | 0.0 | nan | |
| | 0.7033 | 64 | - | 0.0 | nan | |
| | 0.7253 | 66 | - | 0.0 | nan | |
| | 0.7473 | 68 | - | 0.0 | nan | |
| | 0.7692 | 70 | - | 0.0 | nan | |
| | 0.7912 | 72 | - | 0.0 | nan | |
| | 0.8132 | 74 | - | 0.0 | nan | |
| | 0.8352 | 76 | - | 0.0 | nan | |
| | 0.8571 | 78 | - | 0.0 | nan | |
| | 0.8791 | 80 | - | 0.0 | nan | |
| | 0.9011 | 82 | - | 0.0 | nan | |
| | 0.9231 | 84 | - | 0.0 | nan | |
| | 0.9451 | 86 | - | 0.0 | nan | |
| | 0.9670 | 88 | - | 0.0 | nan | |
| | 0.9890 | 90 | - | 0.0 | nan | |
| | 1.0110 | 92 | - | 0.0 | nan | |
| | 1.0330 | 94 | - | 0.0 | nan | |
| | 1.0549 | 96 | - | 0.0 | nan | |
| | 1.0769 | 98 | - | 0.0 | nan | |
| | 1.0989 | 100 | - | 0.0 | nan | |
| | 1.1209 | 102 | - | 0.0 | nan | |
| | 1.1429 | 104 | - | 0.0 | nan | |
| | 1.1648 | 106 | - | 0.0 | nan | |
| | 1.1868 | 108 | - | 0.0 | nan | |
| | 1.2088 | 110 | - | 0.0 | nan | |
| | 1.2308 | 112 | - | 0.0 | nan | |
| | 1.2527 | 114 | - | 0.0 | nan | |
| | 1.2747 | 116 | - | 0.0 | nan | |
| | 1.2967 | 118 | - | 0.0 | nan | |
| | 1.3187 | 120 | - | 0.0 | nan | |
| | 1.3407 | 122 | - | 0.0 | nan | |
| | 1.3626 | 124 | - | 0.0 | nan | |
| | 1.3846 | 126 | - | 0.0 | nan | |
| | 1.4066 | 128 | - | 0.0 | nan | |
| | 1.4286 | 130 | - | 0.0 | nan | |
| | 1.4505 | 132 | - | 0.0 | nan | |
| | 1.4725 | 134 | - | 0.0 | nan | |
| | 1.4945 | 136 | - | 0.0 | nan | |
| | 1.5165 | 138 | - | 0.0 | nan | |
| | 1.5385 | 140 | - | 0.0 | nan | |
| | 1.5604 | 142 | - | 0.0 | nan | |
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| | 1.6703 | 152 | - | 0.0 | nan | |
| | 1.6923 | 154 | - | 0.0 | nan | |
| | 1.7143 | 156 | - | 0.0 | nan | |
| | 1.7363 | 158 | - | 0.0 | nan | |
| | 1.7582 | 160 | - | 0.0 | nan | |
| | 1.7802 | 162 | - | 0.0 | nan | |
| | 1.8022 | 164 | - | 0.0 | nan | |
| | 1.8242 | 166 | - | 0.0 | nan | |
| | 1.8462 | 168 | - | 0.0 | nan | |
| | 1.8681 | 170 | - | 0.0 | nan | |
| | 1.8901 | 172 | - | 0.0 | nan | |
| | 1.9121 | 174 | - | 0.0 | nan | |
| | 1.9341 | 176 | - | 0.0 | nan | |
| | 1.9560 | 178 | - | 0.0 | nan | |
| | 1.9780 | 180 | - | 0.0 | nan | |
| | 2.0 | 182 | - | 0.0 | nan | |
| | 2.0220 | 184 | - | 0.0 | nan | |
| | 2.0440 | 186 | - | 0.0 | nan | |
| | 2.0659 | 188 | - | 0.0 | nan | |
| | 2.0879 | 190 | - | 0.0 | nan | |
| | 2.1099 | 192 | - | 0.0 | nan | |
| | 2.1319 | 194 | - | 0.0 | nan | |
| | 2.1538 | 196 | - | 0.0 | nan | |
| | 2.1758 | 198 | - | 0.0 | nan | |
| | 2.1978 | 200 | - | 0.0 | nan | |
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| | 2.2418 | 204 | - | 0.0 | nan | |
| | 2.2637 | 206 | - | 0.0 | nan | |
| | 2.2857 | 208 | - | 0.0 | nan | |
| | 2.3077 | 210 | - | 0.0 | nan | |
| | 2.3297 | 212 | - | 0.0 | nan | |
| | 2.3516 | 214 | - | 0.0 | nan | |
| | 2.3736 | 216 | - | 0.0 | nan | |
| | 2.3956 | 218 | - | 0.0 | nan | |
| | 2.4176 | 220 | - | 0.0 | nan | |
| | 2.4396 | 222 | - | 0.0 | nan | |
| | 2.4615 | 224 | - | 0.0 | nan | |
| | 2.4835 | 226 | - | 0.0 | nan | |
| | 2.5055 | 228 | - | 0.0 | nan | |
| | 2.5275 | 230 | - | 0.0 | nan | |
| | 2.5495 | 232 | - | 0.0 | nan | |
| | 2.5714 | 234 | - | 0.0 | nan | |
| | 2.5934 | 236 | - | 0.0 | nan | |
| | 2.6154 | 238 | - | 0.0 | nan | |
| | 2.6374 | 240 | - | 0.0 | nan | |
| | 2.6593 | 242 | - | 0.0 | nan | |
| | 2.6813 | 244 | - | 0.0 | nan | |
| | 2.7033 | 246 | - | 0.0 | nan | |
| | 2.7253 | 248 | - | 0.0 | nan | |
| | 2.7473 | 250 | - | 0.0 | nan | |
| | 2.7692 | 252 | - | 0.0 | nan | |
| | 2.7912 | 254 | - | 0.0 | nan | |
| | 2.8132 | 256 | - | 0.0 | nan | |
| | 2.8352 | 258 | - | 0.0 | nan | |
| | 2.8571 | 260 | - | 0.0 | nan | |
| | 2.8791 | 262 | - | 0.0 | nan | |
| | 2.9011 | 264 | - | 0.0 | nan | |
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| | 2.9451 | 268 | - | 0.0 | nan | |
| | 2.9670 | 270 | - | 0.0 | nan | |
| | 2.9890 | 272 | - | 0.0 | nan | |
| | 3.0110 | 274 | - | 0.0 | nan | |
| | 3.0330 | 276 | - | 0.0 | nan | |
| | 3.0549 | 278 | - | 0.0 | nan | |
| | 3.0769 | 280 | - | 0.0 | nan | |
| | 3.0989 | 282 | - | 0.0 | nan | |
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| | 9.9121 | 902 | - | 0.0 | nan | |
| | 9.9341 | 904 | - | 0.0 | nan | |
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| | 10.0 | 910 | - | 0.0 | nan | |
|
|
| * The bold row denotes the saved checkpoint. |
| </details> |
|
|
| ### Framework Versions |
| - Python: 3.10.12 |
| - Sentence Transformers: 3.0.1 |
| - Transformers: 4.41.2 |
| - PyTorch: 2.0.1+cu118 |
| - Accelerate: 0.31.0 |
| - Datasets: 2.20.0 |
| - 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", |
| } |
| ``` |
|
|
| #### CoSENTLoss |
| ```bibtex |
| @online{kexuefm-8847, |
| title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
| author={Su Jianlin}, |
| year={2022}, |
| month={Jan}, |
| url={https://kexue.fm/archives/8847}, |
| } |
| ``` |
|
|
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|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
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| ## Model Card Authors |
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|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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|
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| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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