| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:50 |
| - loss:MultipleNegativesRankingLoss |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| widget: |
| - source_sentence: Two men on bicycles competing in a race. |
| sentences: |
| - People are riding bikes. |
| - A woman is doing a cartwheel. |
| - A few people are catching fish. |
| - source_sentence: A man selling donuts to a customer during a world exhibition event |
| held in the city of Angeles |
| sentences: |
| - An Indian woman is doing her laundry in a lake. |
| - A man selling donuts to a customer. |
| - A woman drinks her coffee in a small cafe. |
| - source_sentence: Kids are on a amusement ride. |
| sentences: |
| - A car is broke down on the side of the road. |
| - A man is wearing a blue shirt |
| - Kids ride an amusement ride. |
| - source_sentence: Two young children in blue jerseys, one with the number 9 and one |
| with the number 2 are standing on wooden steps in a bathroom and washing their |
| hands in a sink. |
| sentences: |
| - A woman is doing a cartwheel. |
| - Two kids in jackets walk to school. |
| - Two kids in numbered jerseys wash their hands. |
| - source_sentence: Two women having drinks and smoking cigarettes at the bar. |
| sentences: |
| - boys play football |
| - Three women are at a bar. |
| - Two women are at a bar. |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy@1 |
| - cosine_accuracy@3 |
| - cosine_accuracy@5 |
| - cosine_accuracy@10 |
| - cosine_precision@1 |
| - cosine_precision@3 |
| - cosine_precision@5 |
| - cosine_precision@10 |
| - cosine_recall@1 |
| - cosine_recall@3 |
| - cosine_recall@5 |
| - cosine_recall@10 |
| - cosine_ndcg@10 |
| - cosine_mrr@10 |
| - cosine_map@100 |
| model-index: |
| - name: all-MiniLM-L6-v2 finetuned on AllNLI |
| results: |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: NanoMSMARCO |
| type: NanoMSMARCO |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.36 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.52 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.58 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.8 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.36 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.1733333333333333 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.11599999999999999 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.08 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.36 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.52 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.58 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.8 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.5537649594026555 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.47934920634920636 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.4904052935766491 |
| name: Cosine Map@100 |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: NanoNFCorpus |
| type: NanoNFCorpus |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.4 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.6 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.62 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.72 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.4 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.35999999999999993 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.324 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.276 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.03458800957047009 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.06249836236458624 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.08046568973676278 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.13259147712553063 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.32368179953782333 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.4918571428571428 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.1389531634193327 |
| name: Cosine Map@100 |
| - task: |
| type: nano-beir |
| name: Nano BEIR |
| dataset: |
| name: NanoBEIR mean |
| type: NanoBEIR_mean |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.38 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.56 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.6 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.76 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.38 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.2666666666666666 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.22 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.17800000000000002 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.19729400478523504 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.29124918118229315 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.3302328448683814 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.46629573856276535 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.4387233794702394 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.4856031746031746 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.3146792284979909 |
| name: Cosine Map@100 |
| --- |
| |
| # all-MiniLM-L6-v2 finetuned on AllNLI |
|
|
| 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) on the all-nli dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval. |
|
|
| ## 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 1110a243fdf4706b3f48f1d95db1a4f5529b4d41 --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 384 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Supported Modality:** Text |
| - **Training Dataset:** |
| - all-nli |
| - **Language:** en |
| - **License:** apache-2.0 |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'}) |
| (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', '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("Hyperakan/all-MiniLM-L6-v2-smoke") |
| # Run inference |
| queries = [ |
| 'Two women having drinks and smoking cigarettes at the bar.', |
| ] |
| documents = [ |
| 'Two women are at a bar.', |
| 'Three women are at a bar.', |
| 'boys play football', |
| ] |
| query_embeddings = model.encode_query(queries) |
| document_embeddings = model.encode_document(documents) |
| print(query_embeddings.shape, document_embeddings.shape) |
| # [1, 384] [3, 384] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(query_embeddings, document_embeddings) |
| print(similarities) |
| # tensor([[ 0.7893, 0.6667, -0.1284]]) |
| ``` |
| <!-- |
| ### 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 |
|
|
| #### Information Retrieval |
|
|
| * Datasets: `NanoMSMARCO` and `NanoNFCorpus` |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) |
|
|
| | Metric | NanoMSMARCO | NanoNFCorpus | |
| |:--------------------|:------------|:-------------| |
| | cosine_accuracy@1 | 0.36 | 0.4 | |
| | cosine_accuracy@3 | 0.52 | 0.6 | |
| | cosine_accuracy@5 | 0.58 | 0.62 | |
| | cosine_accuracy@10 | 0.8 | 0.72 | |
| | cosine_precision@1 | 0.36 | 0.4 | |
| | cosine_precision@3 | 0.1733 | 0.36 | |
| | cosine_precision@5 | 0.116 | 0.324 | |
| | cosine_precision@10 | 0.08 | 0.276 | |
| | cosine_recall@1 | 0.36 | 0.0346 | |
| | cosine_recall@3 | 0.52 | 0.0625 | |
| | cosine_recall@5 | 0.58 | 0.0805 | |
| | cosine_recall@10 | 0.8 | 0.1326 | |
| | **cosine_ndcg@10** | **0.5538** | **0.3237** | |
| | cosine_mrr@10 | 0.4793 | 0.4919 | |
| | cosine_map@100 | 0.4904 | 0.139 | |
| |
| #### Nano BEIR |
| |
| * Dataset: `NanoBEIR_mean` |
| * Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.NanoBEIREvaluator) with these parameters: |
| ```json |
| { |
| "dataset_names": [ |
| "msmarco", |
| "nfcorpus" |
| ], |
| "dataset_id": "sentence-transformers/NanoBEIR-en" |
| } |
| ``` |
| |
| | Metric | Value | |
| |:--------------------|:-----------| |
| | cosine_accuracy@1 | 0.38 | |
| | cosine_accuracy@3 | 0.56 | |
| | cosine_accuracy@5 | 0.6 | |
| | cosine_accuracy@10 | 0.76 | |
| | cosine_precision@1 | 0.38 | |
| | cosine_precision@3 | 0.2667 | |
| | cosine_precision@5 | 0.22 | |
| | cosine_precision@10 | 0.178 | |
| | cosine_recall@1 | 0.1973 | |
| | cosine_recall@3 | 0.2912 | |
| | cosine_recall@5 | 0.3302 | |
| | cosine_recall@10 | 0.4663 | |
| | **cosine_ndcg@10** | **0.4387** | |
| | cosine_mrr@10 | 0.4856 | |
| | cosine_map@100 | 0.3147 | |
| |
| <!-- |
| ## 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 |
| |
| #### all-nli |
| |
| * Dataset: all-nli |
| * Size: 50 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 50 samples: |
| | | anchor | positive | negative | |
| |:---------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | modality | text | text | text | |
| | details | <ul><li>min: 8 tokens</li><li>mean: 21.7 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.4 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.34 tokens</li><li>max: 30 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| |
| | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> | |
| | <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> | |
| | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</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", |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
| |
| ### Evaluation Dataset |
|
|
| #### all-nli |
|
|
| * Dataset: all-nli |
| * Size: 20 evaluation samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 20 samples: |
| | | anchor | positive | negative | |
| |:---------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | modality | text | text | text | |
| | details | <ul><li>min: 9 tokens</li><li>mean: 19.3 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.55 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.05 tokens</li><li>max: 15 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------| |
| | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> | |
| | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> | |
| | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</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", |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `learning_rate`: 2e-05 |
| - `weight_decay`: 0.01 |
| - `num_train_epochs`: 1 |
| - `max_steps`: 1 |
| - `warmup_ratio`: 0.1 |
| - `seed`: 12 |
| - `bf16`: True |
| - `load_best_model_at_end`: True |
| - `batch_sampler`: no_duplicates |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `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`: 2e-05 |
| - `weight_decay`: 0.01 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1.0 |
| - `num_train_epochs`: 1 |
| - `max_steps`: 1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `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`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `no_cuda`: False |
| - `use_cpu`: False |
| - `use_mps_device`: False |
| - `seed`: 12 |
| - `data_seed`: None |
| - `jit_mode_eval`: False |
| - `bf16`: True |
| - `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`: 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} |
| - `parallelism_config`: None |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch |
| - `optim_args`: None |
| - `adafactor`: False |
| - `group_by_length`: False |
| - `length_column_name`: length |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `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`: None |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `include_inputs_for_metrics`: False |
| - `include_for_metrics`: [] |
| - `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 |
| - `include_tokens_per_second`: False |
| - `include_num_input_tokens_seen`: no |
| - `neftune_noise_alpha`: None |
| - `optim_target_modules`: None |
| - `batch_eval_metrics`: False |
| - `eval_on_start`: False |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: True |
| - `prompts`: None |
| - `batch_sampler`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 | |
| |:--------:|:-----:|:-------------:|:---------------:|:--------------------------:|:---------------------------:|:----------------------------:| |
| | -1 | -1 | - | - | 0.5538 | 0.3237 | 0.4387 | |
| | **0.25** | **1** | **0.5675** | **0.1029** | **0.554** | **0.3235** | **0.4387** | |
| | -1 | -1 | - | - | 0.5538 | 0.3237 | 0.4387 | |
| |
| * The bold row denotes the saved checkpoint. |
| |
| ### Training Time |
| - **Training**: 12.6 seconds |
| - **Evaluation**: 8.9 seconds |
| - **Total**: 21.5 seconds |
| |
| ### Framework Versions |
| - Python: 3.12.13 |
| - Sentence Transformers: 5.6.0 |
| - Transformers: 4.57.6 |
| - PyTorch: 2.5.1+cu121 |
| - Accelerate: 1.14.0 |
| - Datasets: 5.0.0 |
| - Tokenizers: 0.22.2 |
| |
| ## 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{oord2019representationlearningcontrastivepredictive, |
| title={Representation Learning with Contrastive Predictive Coding}, |
| author={Aaron van den Oord and Yazhe Li and Oriol Vinyals}, |
| year={2019}, |
| eprint={1807.03748}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG}, |
| url={https://arxiv.org/abs/1807.03748}, |
| } |
| ``` |
| |
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