| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:11641 |
| - loss:MultipleNegativesRankingLoss |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| widget: |
| - source_sentence: 2026 for Djokovic? |
| sentences: |
| - What is the serve speed for he? |
| - momentum for Djokovic? |
| - 2026 for Sinner? |
| - source_sentence: What are the match time for Djokovic? |
| sentences: |
| - Show me how many winners |
| - What is at stake for Aryna Sabalenka in the next round? |
| - What is the key factors for Djokovic? |
| - source_sentence: What's the points won for Djokovic? |
| sentences: |
| - What about he's aggressive? |
| - What about the player's won? |
| - Show me how many winners |
| - source_sentence: What's the title count for Djokovic? |
| sentences: |
| - What's the winner count for Djokovic? |
| - How does Ben the player's 2025 form compare to their career average? |
| - What's the title count for Sinner? |
| - source_sentence: What is the break point conversion for Sinner? |
| sentences: |
| - Show me how many winners |
| - Show me how many winners |
| - service for Sinner? |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # 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 c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
| - **Maximum Sequence Length:** 256 tokens |
| - **Output Dimensionality:** 384 dimensions |
| - **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': 256, 'do_lower_case': False, 'architecture': '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("GozdeA/tennis-multi-return-catboost-v3") |
| # Run inference |
| sentences = [ |
| 'What is the break point conversion for Sinner?', |
| 'Show me how many winners', |
| 'service for Sinner?', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 384] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.5908, 0.2891], |
| # [0.5908, 1.0000, 0.4187], |
| # [0.2891, 0.4187, 1.0000]]) |
| ``` |
|
|
| <!-- |
| ### 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.* |
| --> |
|
|
| <!-- |
| ## 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: 11,641 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: 4 tokens</li><li>mean: 10.77 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.62 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.51 tokens</li><li>max: 26 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:----------------------------------------------------------------------|:--------------------------------------------------------|:-----------------------------------------------------| |
| | <code>What about he's odds?</code> | <code>momentum shift?</code> | <code>What happened to he?</code> | |
| | <code>How far has Nardi advanced at Wimbledon in his best run?</code> | <code>how many titles?</code> | <code>What is the what court for he?</code> | |
| | <code>How effective is Swiatek's return in the match?</code> | <code>How effective is he's return in the match?</code> | <code>How effective is his return in the game</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 |
|
|
| #### Unnamed Dataset |
|
|
| * Size: 2,911 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: 4 tokens</li><li>mean: 11.06 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.7 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.53 tokens</li><li>max: 28 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:------------------------------------------|:------------------------------------|:---------------------------------------------| |
| | <code>what venue</code> | <code>Show me what venue</code> | <code>venue time?</code> | |
| | <code>2025 for he?</code> | <code>how many titles?</code> | <code>Show me which court</code> | |
| | <code>What about Djokovic's debut?</code> | <code>What about he's debut?</code> | <code>What about Djokovic's momentum?</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 |
|
|
| - `per_device_train_batch_size`: 16 |
| - `learning_rate`: 2e-05 |
| - `num_train_epochs`: 15 |
| - `warmup_ratio`: 0.1 |
| - `fp16`: True |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: no |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 16 |
| - `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 |
| - `torch_empty_cache_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`: 15 |
| - `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`: 42 |
| - `data_seed`: None |
| - `jit_mode_eval`: 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`: 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} |
| - `parallelism_config`: None |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch_fused |
| - `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`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| <details><summary>Click to expand</summary> |
|
|
| | Epoch | Step | Training Loss | |
| |:-------:|:-----:|:-------------:| |
| | 0.0687 | 50 | 5.002 | |
| | 0.1374 | 100 | 4.122 | |
| | 0.2060 | 150 | 3.3282 | |
| | 0.2747 | 200 | 2.5309 | |
| | 0.3434 | 250 | 1.9021 | |
| | 0.4121 | 300 | 1.7012 | |
| | 0.4808 | 350 | 1.4657 | |
| | 0.5495 | 400 | 1.433 | |
| | 0.6181 | 450 | 1.5156 | |
| | 0.6868 | 500 | 1.3941 | |
| | 0.7555 | 550 | 1.2544 | |
| | 0.8242 | 600 | 1.1585 | |
| | 0.8929 | 650 | 1.0916 | |
| | 0.9615 | 700 | 0.9743 | |
| | 1.0302 | 750 | 1.0443 | |
| | 1.0989 | 800 | 0.9942 | |
| | 1.1676 | 850 | 1.0508 | |
| | 1.2363 | 900 | 0.9211 | |
| | 1.3049 | 950 | 0.9522 | |
| | 1.3736 | 1000 | 0.804 | |
| | 1.4423 | 1050 | 0.8645 | |
| | 1.5110 | 1100 | 0.8335 | |
| | 1.5797 | 1150 | 0.7337 | |
| | 1.6484 | 1200 | 0.7857 | |
| | 1.7170 | 1250 | 0.8482 | |
| | 1.7857 | 1300 | 0.7211 | |
| | 1.8544 | 1350 | 0.7442 | |
| | 1.9231 | 1400 | 0.7557 | |
| | 1.9918 | 1450 | 0.7323 | |
| | 2.0604 | 1500 | 0.677 | |
| | 2.1291 | 1550 | 0.6635 | |
| | 2.1978 | 1600 | 0.71 | |
| | 2.2665 | 1650 | 0.6193 | |
| | 2.3352 | 1700 | 0.6792 | |
| | 2.4038 | 1750 | 0.7151 | |
| | 2.4725 | 1800 | 0.6825 | |
| | 2.5412 | 1850 | 0.6452 | |
| | 2.6099 | 1900 | 0.666 | |
| | 2.6786 | 1950 | 0.5733 | |
| | 2.7473 | 2000 | 0.5546 | |
| | 2.8159 | 2050 | 0.6443 | |
| | 2.8846 | 2100 | 0.6835 | |
| | 2.9533 | 2150 | 0.6499 | |
| | 3.0220 | 2200 | 0.6229 | |
| | 3.0907 | 2250 | 0.6151 | |
| | 3.1593 | 2300 | 0.539 | |
| | 3.2280 | 2350 | 0.5997 | |
| | 3.2967 | 2400 | 0.571 | |
| | 3.3654 | 2450 | 0.6257 | |
| | 3.4341 | 2500 | 0.6222 | |
| | 3.5027 | 2550 | 0.6102 | |
| | 3.5714 | 2600 | 0.6575 | |
| | 3.6401 | 2650 | 0.5844 | |
| | 3.7088 | 2700 | 0.5439 | |
| | 3.7775 | 2750 | 0.5528 | |
| | 3.8462 | 2800 | 0.5894 | |
| | 3.9148 | 2850 | 0.6576 | |
| | 3.9835 | 2900 | 0.6063 | |
| | 4.0522 | 2950 | 0.5556 | |
| | 4.1209 | 3000 | 0.5872 | |
| | 4.1896 | 3050 | 0.544 | |
| | 4.2582 | 3100 | 0.5114 | |
| | 4.3269 | 3150 | 0.587 | |
| | 4.3956 | 3200 | 0.5392 | |
| | 4.4643 | 3250 | 0.5846 | |
| | 4.5330 | 3300 | 0.6077 | |
| | 4.6016 | 3350 | 0.6597 | |
| | 4.6703 | 3400 | 0.5425 | |
| | 4.7390 | 3450 | 0.5493 | |
| | 4.8077 | 3500 | 0.5291 | |
| | 4.8764 | 3550 | 0.5145 | |
| | 4.9451 | 3600 | 0.5534 | |
| | 5.0137 | 3650 | 0.5018 | |
| | 5.0824 | 3700 | 0.4948 | |
| | 5.1511 | 3750 | 0.553 | |
| | 5.2198 | 3800 | 0.5772 | |
| | 5.2885 | 3850 | 0.5264 | |
| | 5.3571 | 3900 | 0.5516 | |
| | 5.4258 | 3950 | 0.5303 | |
| | 5.4945 | 4000 | 0.5213 | |
| | 5.5632 | 4050 | 0.5558 | |
| | 5.6319 | 4100 | 0.4956 | |
| | 5.7005 | 4150 | 0.6035 | |
| | 5.7692 | 4200 | 0.5706 | |
| | 5.8379 | 4250 | 0.4922 | |
| | 5.9066 | 4300 | 0.5965 | |
| | 5.9753 | 4350 | 0.5143 | |
| | 6.0440 | 4400 | 0.5798 | |
| | 6.1126 | 4450 | 0.5219 | |
| | 6.1813 | 4500 | 0.5803 | |
| | 6.25 | 4550 | 0.5035 | |
| | 6.3187 | 4600 | 0.5534 | |
| | 6.3874 | 4650 | 0.546 | |
| | 6.4560 | 4700 | 0.525 | |
| | 6.5247 | 4750 | 0.4751 | |
| | 6.5934 | 4800 | 0.5085 | |
| | 6.6621 | 4850 | 0.5282 | |
| | 6.7308 | 4900 | 0.5845 | |
| | 6.7995 | 4950 | 0.5153 | |
| | 6.8681 | 5000 | 0.5399 | |
| | 6.9368 | 5050 | 0.5532 | |
| | 7.0055 | 5100 | 0.5005 | |
| | 7.0742 | 5150 | 0.5273 | |
| | 7.1429 | 5200 | 0.5212 | |
| | 7.2115 | 5250 | 0.5245 | |
| | 7.2802 | 5300 | 0.5075 | |
| | 7.3489 | 5350 | 0.5687 | |
| | 7.4176 | 5400 | 0.4674 | |
| | 7.4863 | 5450 | 0.5115 | |
| | 7.5549 | 5500 | 0.4938 | |
| | 7.6236 | 5550 | 0.5059 | |
| | 7.6923 | 5600 | 0.5065 | |
| | 7.7610 | 5650 | 0.5252 | |
| | 7.8297 | 5700 | 0.4852 | |
| | 7.8984 | 5750 | 0.48 | |
| | 7.9670 | 5800 | 0.5503 | |
| | 8.0357 | 5850 | 0.5164 | |
| | 8.1044 | 5900 | 0.5756 | |
| | 8.1731 | 5950 | 0.5175 | |
| | 8.2418 | 6000 | 0.5033 | |
| | 8.3104 | 6050 | 0.4992 | |
| | 8.3791 | 6100 | 0.5299 | |
| | 8.4478 | 6150 | 0.4862 | |
| | 8.5165 | 6200 | 0.548 | |
| | 8.5852 | 6250 | 0.454 | |
| | 8.6538 | 6300 | 0.4941 | |
| | 8.7225 | 6350 | 0.5088 | |
| | 8.7912 | 6400 | 0.5065 | |
| | 8.8599 | 6450 | 0.4921 | |
| | 8.9286 | 6500 | 0.4756 | |
| | 8.9973 | 6550 | 0.5258 | |
| | 9.0659 | 6600 | 0.4658 | |
| | 9.1346 | 6650 | 0.4894 | |
| | 9.2033 | 6700 | 0.5097 | |
| | 9.2720 | 6750 | 0.493 | |
| | 9.3407 | 6800 | 0.5311 | |
| | 9.4093 | 6850 | 0.5157 | |
| | 9.4780 | 6900 | 0.5142 | |
| | 9.5467 | 6950 | 0.4664 | |
| | 9.6154 | 7000 | 0.528 | |
| | 9.6841 | 7050 | 0.5645 | |
| | 9.7527 | 7100 | 0.5214 | |
| | 9.8214 | 7150 | 0.4777 | |
| | 9.8901 | 7200 | 0.5449 | |
| | 9.9588 | 7250 | 0.492 | |
| | 10.0275 | 7300 | 0.4591 | |
| | 10.0962 | 7350 | 0.4576 | |
| | 10.1648 | 7400 | 0.4692 | |
| | 10.2335 | 7450 | 0.5415 | |
| | 10.3022 | 7500 | 0.4803 | |
| | 10.3709 | 7550 | 0.5487 | |
| | 10.4396 | 7600 | 0.5706 | |
| | 10.5082 | 7650 | 0.4815 | |
| | 10.5769 | 7700 | 0.4585 | |
| | 10.6456 | 7750 | 0.4861 | |
| | 10.7143 | 7800 | 0.4247 | |
| | 10.7830 | 7850 | 0.4906 | |
| | 10.8516 | 7900 | 0.5371 | |
| | 10.9203 | 7950 | 0.5393 | |
| | 10.9890 | 8000 | 0.4788 | |
| | 11.0577 | 8050 | 0.5038 | |
| | 11.1264 | 8100 | 0.4838 | |
| | 11.1951 | 8150 | 0.515 | |
| | 11.2637 | 8200 | 0.5299 | |
| | 11.3324 | 8250 | 0.5044 | |
| | 11.4011 | 8300 | 0.5045 | |
| | 11.4698 | 8350 | 0.465 | |
| | 11.5385 | 8400 | 0.5253 | |
| | 11.6071 | 8450 | 0.4517 | |
| | 11.6758 | 8500 | 0.5048 | |
| | 11.7445 | 8550 | 0.4733 | |
| | 11.8132 | 8600 | 0.47 | |
| | 11.8819 | 8650 | 0.4552 | |
| | 11.9505 | 8700 | 0.4203 | |
| | 12.0192 | 8750 | 0.395 | |
| | 12.0879 | 8800 | 0.5411 | |
| | 12.1566 | 8850 | 0.4911 | |
| | 12.2253 | 8900 | 0.4641 | |
| | 12.2940 | 8950 | 0.4608 | |
| | 12.3626 | 9000 | 0.4839 | |
| | 12.4313 | 9050 | 0.4491 | |
| | 12.5 | 9100 | 0.517 | |
| | 12.5687 | 9150 | 0.5031 | |
| | 12.6374 | 9200 | 0.4869 | |
| | 12.7060 | 9250 | 0.4856 | |
| | 12.7747 | 9300 | 0.4754 | |
| | 12.8434 | 9350 | 0.5167 | |
| | 12.9121 | 9400 | 0.5004 | |
| | 12.9808 | 9450 | 0.5293 | |
| | 13.0495 | 9500 | 0.4566 | |
| | 13.1181 | 9550 | 0.477 | |
| | 13.1868 | 9600 | 0.4501 | |
| | 13.2555 | 9650 | 0.4791 | |
| | 13.3242 | 9700 | 0.4746 | |
| | 13.3929 | 9750 | 0.4702 | |
| | 13.4615 | 9800 | 0.469 | |
| | 13.5302 | 9850 | 0.5046 | |
| | 13.5989 | 9900 | 0.4895 | |
| | 13.6676 | 9950 | 0.5223 | |
| | 13.7363 | 10000 | 0.4245 | |
| | 13.8049 | 10050 | 0.4701 | |
| | 13.8736 | 10100 | 0.4548 | |
| | 13.9423 | 10150 | 0.4998 | |
| | 14.0110 | 10200 | 0.4345 | |
| | 14.0797 | 10250 | 0.4371 | |
| | 14.1484 | 10300 | 0.5009 | |
| | 14.2170 | 10350 | 0.4816 | |
| | 14.2857 | 10400 | 0.4665 | |
| | 14.3544 | 10450 | 0.5047 | |
| | 14.4231 | 10500 | 0.5132 | |
| | 14.4918 | 10550 | 0.473 | |
| | 14.5604 | 10600 | 0.4387 | |
| | 14.6291 | 10650 | 0.4775 | |
| | 14.6978 | 10700 | 0.4522 | |
| | 14.7665 | 10750 | 0.4807 | |
| | 14.8352 | 10800 | 0.482 | |
| | 14.9038 | 10850 | 0.4625 | |
| | 14.9725 | 10900 | 0.5052 | |
|
|
| </details> |
|
|
| ### Framework Versions |
| - Python: 3.12.12 |
| - Sentence Transformers: 5.0.0 |
| - Transformers: 4.57.6 |
| - PyTorch: 2.10.0+cu128 |
| - Accelerate: 1.13.0 |
| - Datasets: 4.0.0 |
| - 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{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.* |
| --> |
