| | --- |
| | base_model: thenlper/gte-small |
| | datasets: |
| | - sentence-transformers/all-nli |
| | language: |
| | - en |
| | library_name: sentence-transformers |
| | license: apache-2.0 |
| | metrics: |
| | - cosine_accuracy |
| | - dot_accuracy |
| | - manhattan_accuracy |
| | - euclidean_accuracy |
| | - max_accuracy |
| | pipeline_tag: sentence-similarity |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:100000 |
| | - loss:MultipleNegativesRankingLoss |
| | widget: |
| | - source_sentence: A man is jumping unto his filthy bed. |
| | sentences: |
| | - A young male is looking at a newspaper while 2 females walks past him. |
| | - The bed is dirty. |
| | - The man is on the moon. |
| | - source_sentence: A carefully balanced male stands on one foot near a clean ocean |
| | beach area. |
| | sentences: |
| | - A man is ouside near the beach. |
| | - Three policemen patrol the streets on bikes |
| | - A man is sitting on his couch. |
| | - source_sentence: The man is wearing a blue shirt. |
| | sentences: |
| | - Near the trashcan the man stood and smoked |
| | - A man in a blue shirt leans on a wall beside a road with a blue van and red car |
| | with water in the background. |
| | - A man in a black shirt is playing a guitar. |
| | - source_sentence: The girls are outdoors. |
| | sentences: |
| | - Two girls riding on an amusement part ride. |
| | - a guy laughs while doing laundry |
| | - Three girls are standing together in a room, one is listening, one is writing |
| | on a wall and the third is talking to them. |
| | - source_sentence: A construction worker peeking out of a manhole while his coworker |
| | sits on the sidewalk smiling. |
| | sentences: |
| | - A worker is looking out of a manhole. |
| | - A man is giving a presentation. |
| | - The workers are both inside the manhole. |
| | model-index: |
| | - name: gte small finetuned on NLI |
| | results: |
| | - task: |
| | type: triplet |
| | name: Triplet |
| | dataset: |
| | name: all nli dev |
| | type: all-nli-dev |
| | metrics: |
| | - type: cosine_accuracy |
| | value: 0.9260328068043743 |
| | name: Cosine Accuracy |
| | - type: dot_accuracy |
| | value: 0.07396719319562577 |
| | name: Dot Accuracy |
| | - type: manhattan_accuracy |
| | value: 0.925273390036452 |
| | name: Manhattan Accuracy |
| | - type: euclidean_accuracy |
| | value: 0.9260328068043743 |
| | name: Euclidean Accuracy |
| | - type: max_accuracy |
| | value: 0.9260328068043743 |
| | name: Max Accuracy |
| | - task: |
| | type: triplet |
| | name: Triplet |
| | dataset: |
| | name: all nli test |
| | type: all-nli-test |
| | metrics: |
| | - type: cosine_accuracy |
| | value: 0.9347858980178544 |
| | name: Cosine Accuracy |
| | - type: dot_accuracy |
| | value: 0.06521410198214556 |
| | name: Dot Accuracy |
| | - type: manhattan_accuracy |
| | value: 0.9331215009835073 |
| | name: Manhattan Accuracy |
| | - type: euclidean_accuracy |
| | value: 0.9347858980178544 |
| | name: Euclidean Accuracy |
| | - type: max_accuracy |
| | value: 0.9347858980178544 |
| | name: Max Accuracy |
| | --- |
| | |
| | # gte small finetuned on NLI |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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:** [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) <!-- at revision 50c7dd33df1027ef560fd504d95e277948c3c886 --> |
| | - **Maximum Sequence Length:** 512 tokens |
| | - **Output Dimensionality:** 384 tokens |
| | - **Similarity Function:** Cosine Similarity |
| | - **Training Dataset:** |
| | - [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/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/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}) |
| | (2): Normalize() |
| | ) |
| | ``` |
| |
|
| | ## 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("SMARTICT/gte-small-finetune-test") |
| | # Run inference |
| | sentences = [ |
| | 'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.', |
| | 'A worker is looking out of a manhole.', |
| | 'The workers are both inside the manhole.', |
| | ] |
| | 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 |
| |
|
| | #### Triplet |
| | * Dataset: `all-nli-dev` |
| | * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
| |
|
| | | Metric | Value | |
| | |:-------------------|:----------| |
| | | cosine_accuracy | 0.926 | |
| | | dot_accuracy | 0.074 | |
| | | manhattan_accuracy | 0.9253 | |
| | | euclidean_accuracy | 0.926 | |
| | | **max_accuracy** | **0.926** | |
| | |
| | #### Triplet |
| | * Dataset: `all-nli-test` |
| | * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
| | |
| | | Metric | Value | |
| | |:-------------------|:-----------| |
| | | cosine_accuracy | 0.9348 | |
| | | dot_accuracy | 0.0652 | |
| | | manhattan_accuracy | 0.9331 | |
| | | euclidean_accuracy | 0.9348 | |
| | | **max_accuracy** | **0.9348** | |
| |
|
| | <!-- |
| | ## 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 |
| |
|
| | #### sentence-transformers/all-nli |
| |
|
| | * Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) |
| | * Size: 100,000 training samples |
| | * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | anchor | positive | negative | |
| | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
| | | type | string | string | string | |
| | | details | <ul><li>min: 7 tokens</li><li>mean: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 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" |
| | } |
| | ``` |
| |
|
| | ### Evaluation Dataset |
| |
|
| | #### sentence-transformers/all-nli |
| |
|
| | * Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) |
| | * Size: 6,584 evaluation samples |
| | * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | anchor | positive | negative | |
| | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | | type | string | string | string | |
| | | details | <ul><li>min: 6 tokens</li><li>mean: 17.95 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.78 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.35 tokens</li><li>max: 29 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" |
| | } |
| | ``` |
| |
|
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| |
|
| | - `eval_strategy`: steps |
| | - `per_device_train_batch_size`: 16 |
| | - `per_device_eval_batch_size`: 16 |
| | - `num_train_epochs`: 1 |
| | - `warmup_ratio`: 0.1 |
| | - `bf16`: True |
| | - `batch_sampler`: no_duplicates |
| | |
| | #### 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 |
| | - `learning_rate`: 5e-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`: 1 |
| | - `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`: 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`: 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`: 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 |
| | - `batch_sampler`: no_duplicates |
| | - `multi_dataset_batch_sampler`: proportional |
| |
|
| | </details> |
| |
|
| | ### Training Logs |
| | | Epoch | Step | Training Loss | loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy | |
| | |:-----:|:----:|:-------------:|:------:|:------------------------:|:-------------------------:| |
| | | 0 | 0 | - | - | 0.9160 | - | |
| | | 0.016 | 100 | 1.4107 | 0.6660 | 0.9069 | - | |
| | | 0.032 | 200 | 0.7368 | 0.6155 | 0.8950 | - | |
| | | 0.048 | 300 | 1.0729 | 0.5522 | 0.9054 | - | |
| | | 0.064 | 400 | 0.719 | 0.5647 | 0.8957 | - | |
| | | 0.08 | 500 | 0.7273 | 0.6278 | 0.8829 | - | |
| | | 0.096 | 600 | 0.9222 | 0.5652 | 0.8975 | - | |
| | | 0.112 | 700 | 0.8402 | 0.5837 | 0.8947 | - | |
| | | 0.128 | 800 | 0.9511 | 0.6110 | 0.8864 | - | |
| | | 0.144 | 900 | 1.0713 | 0.5923 | 0.8852 | - | |
| | | 0.16 | 1000 | 0.9495 | 0.5216 | 0.8888 | - | |
| | | 0.176 | 1100 | 1.0079 | 0.6263 | 0.8777 | - | |
| | | 0.192 | 1200 | 0.9195 | 0.5970 | 0.8777 | - | |
| | | 0.208 | 1300 | 0.8018 | 0.6342 | 0.8765 | - | |
| | | 0.224 | 1400 | 0.7124 | 0.6462 | 0.8764 | - | |
| | | 0.24 | 1500 | 0.709 | 0.5232 | 0.8964 | - | |
| | | 0.256 | 1600 | 0.6055 | 0.6109 | 0.8838 | - | |
| | | 0.272 | 1700 | 0.7887 | 0.6620 | 0.8768 | - | |
| | | 0.288 | 1800 | 0.789 | 0.5957 | 0.8829 | - | |
| | | 0.304 | 1900 | 0.6711 | 0.5377 | 0.8946 | - | |
| | | 0.32 | 2000 | 0.6086 | 0.5596 | 0.8932 | - | |
| | | 0.336 | 2100 | 0.5067 | 0.5676 | 0.8861 | - | |
| | | 0.352 | 2200 | 0.5387 | 0.5704 | 0.8900 | - | |
| | | 0.368 | 2300 | 0.6574 | 0.5308 | 0.8890 | - | |
| | | 0.384 | 2400 | 0.6232 | 0.5051 | 0.8928 | - | |
| | | 0.4 | 2500 | 0.6045 | 0.5179 | 0.9023 | - | |
| | | 0.416 | 2600 | 0.4795 | 0.4766 | 0.8960 | - | |
| | | 0.432 | 2700 | 0.7372 | 0.5463 | 0.8979 | - | |
| | | 0.448 | 2800 | 0.7593 | 0.5337 | 0.8878 | - | |
| | | 0.464 | 2900 | 0.7384 | 0.5203 | 0.8923 | - | |
| | | 0.48 | 3000 | 0.6336 | 0.5099 | 0.8897 | - | |
| | | 0.496 | 3100 | 0.6634 | 0.4803 | 0.8954 | - | |
| | | 0.512 | 3200 | 0.5443 | 0.4524 | 0.9048 | - | |
| | | 0.528 | 3300 | 0.5292 | 0.4232 | 0.9104 | - | |
| | | 0.544 | 3400 | 0.4633 | 0.4414 | 0.9093 | - | |
| | | 0.56 | 3500 | 0.4442 | 0.4393 | 0.9087 | - | |
| | | 0.576 | 3600 | 0.4443 | 0.4178 | 0.9128 | - | |
| | | 0.592 | 3700 | 0.4736 | 0.4123 | 0.9134 | - | |
| | | 0.608 | 3800 | 0.4077 | 0.4025 | 0.9174 | - | |
| | | 0.624 | 3900 | 0.4069 | 0.4032 | 0.9156 | - | |
| | | 0.64 | 4000 | 0.6939 | 0.4353 | 0.9146 | - | |
| | | 0.656 | 4100 | 0.865 | 0.4154 | 0.9172 | - | |
| | | 0.672 | 4200 | 0.8518 | 0.3925 | 0.9172 | - | |
| | | 0.688 | 4300 | 0.5989 | 0.3864 | 0.9190 | - | |
| | | 0.704 | 4400 | 0.5399 | 0.3679 | 0.9197 | - | |
| | | 0.72 | 4500 | 0.497 | 0.3766 | 0.9221 | - | |
| | | 0.736 | 4600 | 0.585 | 0.3708 | 0.9228 | - | |
| | | 0.752 | 4700 | 0.6454 | 0.3608 | 0.9203 | - | |
| | | 0.768 | 4800 | 0.5414 | 0.3593 | 0.9213 | - | |
| | | 0.784 | 4900 | 0.4648 | 0.3634 | 0.9210 | - | |
| | | 0.8 | 5000 | 0.5781 | 0.3782 | 0.9216 | - | |
| | | 0.816 | 5100 | 0.4401 | 0.3662 | 0.9227 | - | |
| | | 0.832 | 5200 | 0.5241 | 0.3595 | 0.9215 | - | |
| | | 0.848 | 5300 | 0.459 | 0.3618 | 0.9215 | - | |
| | | 0.864 | 5400 | 0.5529 | 0.3693 | 0.9216 | - | |
| | | 0.88 | 5500 | 0.5202 | 0.3573 | 0.9218 | - | |
| | | 0.896 | 5600 | 0.4703 | 0.3529 | 0.9231 | - | |
| | | 0.912 | 5700 | 0.5658 | 0.3513 | 0.9245 | - | |
| | | 0.928 | 5800 | 0.5016 | 0.3491 | 0.9236 | - | |
| | | 0.944 | 5900 | 0.6306 | 0.3492 | 0.9257 | - | |
| | | 0.96 | 6000 | 0.6721 | 0.3507 | 0.9266 | - | |
| | | 0.976 | 6100 | 0.586 | 0.3509 | 0.9257 | - | |
| | | 0.992 | 6200 | 0.0014 | 0.3511 | 0.9260 | - | |
| | | 1.0 | 6250 | - | - | - | 0.9348 | |
| |
|
| |
|
| | ### Framework Versions |
| | - Python: 3.10.12 |
| | - Sentence Transformers: 3.0.1 |
| | - Transformers: 4.42.4 |
| | - PyTorch: 2.4.0+cu121 |
| | - Accelerate: 0.32.1 |
| | - Datasets: 2.21.0 |
| | - 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} |
| | } |
| | ``` |
| |
|
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