| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:11600 |
| - loss:MultipleNegativesRankingLoss |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| widget: |
| - source_sentence: What are the serving today for Djokovic? |
| sentences: |
| - serving for Djokovic? |
| - last for Djokovic? |
| - What is the serving today for Djokovic? |
| - source_sentence: What is Alcaraz’s total games won? |
| sentences: |
| - how many winners? |
| - What about Sinner's winning? |
| - slam for Djokovic? |
| - source_sentence: What's the total time on court for both players? |
| sentences: |
| - form shift? |
| - What about Djokovic's odds? |
| - Show me how old |
| - source_sentence: Did Nardi beat the US Open at any point? |
| sentences: |
| - faults for Djokovic? |
| - What is the how many winners for Djokovic? |
| - Show me how many titles |
| - source_sentence: Show me previous game result |
| sentences: |
| - How is the tactical battle between the player and Amanda Anismova playing out? |
| - Show me how many winners |
| - what venue |
| 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-v4") |
| # Run inference |
| sentences = [ |
| 'Show me previous game result', |
| 'what venue', |
| 'How is the tactical battle between the player and Amanda Anismova playing out?', |
| ] |
| 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, 0.6952, -0.0128], |
| # [ 0.6952, 1.0000, 0.0505], |
| # [-0.0128, 0.0505, 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: 11,600 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: 10.75 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.66 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.45 tokens</li><li>max: 23 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:---------------------------------------------------|:--------------------------------------------------|:-----------------------------------------------------------------------------| |
| | <code>What is the this season for Djokovic?</code> | <code>What's the this season for Djokovic?</code> | <code>What is the attacking this set for Djokovic?</code> | |
| | <code>who is projected?</code> | <code>momentum shift?</code> | <code>How does she's path to this round compare to Amanda Anismova's?</code> | |
| | <code>What's the sets won for Sinner?</code> | <code>Show me how many winners</code> | <code>What's the last year for Djokovic?</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: 2,900 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: 10.63 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.64 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.24 tokens</li><li>max: 24 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:-------------------------------------------------------|:-------------------------------------------------------|:------------------------------------------------------| |
| | <code>What about Djokovic's games?</code> | <code>What's the how many winners for Djokovic?</code> | <code>ranking for the player?</code> | |
| | <code>What is the next match for Djokovic?</code> | <code>What are the next match for Djokovic?</code> | <code>What is the pre match for Djokovic?</code> | |
| | <code>What are the gaining momentum for Sinner?</code> | <code>What is the gaining momentum for Sinner?</code> | <code>What are the gaining control for Sinner?</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 | |
| |:-------:|:-----:|:-------------:| |
| | 0.0690 | 50 | 5.1095 | |
| | 0.1379 | 100 | 3.9909 | |
| | 0.2069 | 150 | 3.1963 | |
| | 0.2759 | 200 | 2.3301 | |
| | 0.3448 | 250 | 1.9904 | |
| | 0.4138 | 300 | 1.6705 | |
| | 0.4828 | 350 | 1.5659 | |
| | 0.5517 | 400 | 1.5497 | |
| | 0.6207 | 450 | 1.3563 | |
| | 0.6897 | 500 | 1.2982 | |
| | 0.7586 | 550 | 1.2509 | |
| | 0.8276 | 600 | 1.1737 | |
| | 0.8966 | 650 | 1.1486 | |
| | 0.9655 | 700 | 1.192 | |
| | 1.0345 | 750 | 0.9715 | |
| | 1.1034 | 800 | 1.0054 | |
| | 1.1724 | 850 | 1.0102 | |
| | 1.2414 | 900 | 0.9393 | |
| | 1.3103 | 950 | 0.9119 | |
| | 1.3793 | 1000 | 0.8589 | |
| | 1.4483 | 1050 | 0.9049 | |
| | 1.5172 | 1100 | 0.8774 | |
| | 1.5862 | 1150 | 0.8488 | |
| | 1.6552 | 1200 | 0.8382 | |
| | 1.7241 | 1250 | 0.7437 | |
| | 1.7931 | 1300 | 0.8023 | |
| | 1.8621 | 1350 | 0.7775 | |
| | 1.9310 | 1400 | 0.7756 | |
| | 2.0 | 1450 | 0.7273 | |
| | 2.0690 | 1500 | 0.6275 | |
| | 2.1379 | 1550 | 0.7331 | |
| | 2.2069 | 1600 | 0.629 | |
| | 2.2759 | 1650 | 0.7127 | |
| | 2.3448 | 1700 | 0.6503 | |
| | 2.4138 | 1750 | 0.7082 | |
| | 2.4828 | 1800 | 0.6939 | |
| | 2.5517 | 1850 | 0.6993 | |
| | 2.6207 | 1900 | 0.7067 | |
| | 2.6897 | 1950 | 0.6622 | |
| | 2.7586 | 2000 | 0.6499 | |
| | 2.8276 | 2050 | 0.6923 | |
| | 2.8966 | 2100 | 0.6208 | |
| | 2.9655 | 2150 | 0.5925 | |
| | 3.0345 | 2200 | 0.6697 | |
| | 3.1034 | 2250 | 0.6458 | |
| | 3.1724 | 2300 | 0.5709 | |
| | 3.2414 | 2350 | 0.5987 | |
| | 3.3103 | 2400 | 0.6252 | |
| | 3.3793 | 2450 | 0.6377 | |
| | 3.4483 | 2500 | 0.5739 | |
| | 3.5172 | 2550 | 0.6281 | |
| | 3.5862 | 2600 | 0.6186 | |
| | 3.6552 | 2650 | 0.5828 | |
| | 3.7241 | 2700 | 0.678 | |
| | 3.7931 | 2750 | 0.6257 | |
| | 3.8621 | 2800 | 0.5704 | |
| | 3.9310 | 2850 | 0.6151 | |
| | 4.0 | 2900 | 0.5898 | |
| | 4.0690 | 2950 | 0.5277 | |
| | 4.1379 | 3000 | 0.6128 | |
| | 4.2069 | 3050 | 0.6306 | |
| | 4.2759 | 3100 | 0.5739 | |
| | 4.3448 | 3150 | 0.5396 | |
| | 4.4138 | 3200 | 0.617 | |
| | 4.4828 | 3250 | 0.5119 | |
| | 4.5517 | 3300 | 0.6136 | |
| | 4.6207 | 3350 | 0.6303 | |
| | 4.6897 | 3400 | 0.6138 | |
| | 4.7586 | 3450 | 0.6214 | |
| | 4.8276 | 3500 | 0.5686 | |
| | 4.8966 | 3550 | 0.5901 | |
| | 4.9655 | 3600 | 0.6913 | |
| | 5.0345 | 3650 | 0.5706 | |
| | 5.1034 | 3700 | 0.6082 | |
| | 5.1724 | 3750 | 0.4755 | |
| | 5.2414 | 3800 | 0.5777 | |
| | 5.3103 | 3850 | 0.5515 | |
| | 5.3793 | 3900 | 0.5271 | |
| | 5.4483 | 3950 | 0.5816 | |
| | 5.5172 | 4000 | 0.5787 | |
| | 5.5862 | 4050 | 0.568 | |
| | 5.6552 | 4100 | 0.5593 | |
| | 5.7241 | 4150 | 0.542 | |
| | 5.7931 | 4200 | 0.5873 | |
| | 5.8621 | 4250 | 0.5647 | |
| | 5.9310 | 4300 | 0.6369 | |
| | 6.0 | 4350 | 0.5775 | |
| | 6.0690 | 4400 | 0.5324 | |
| | 6.1379 | 4450 | 0.5463 | |
| | 6.2069 | 4500 | 0.5234 | |
| | 6.2759 | 4550 | 0.4921 | |
| | 6.3448 | 4600 | 0.5716 | |
| | 6.4138 | 4650 | 0.6321 | |
| | 6.4828 | 4700 | 0.4881 | |
| | 6.5517 | 4750 | 0.5717 | |
| | 6.6207 | 4800 | 0.5922 | |
| | 6.6897 | 4850 | 0.5289 | |
| | 6.7586 | 4900 | 0.5182 | |
| | 6.8276 | 4950 | 0.5096 | |
| | 6.8966 | 5000 | 0.6062 | |
| | 6.9655 | 5050 | 0.6014 | |
| | 7.0345 | 5100 | 0.5033 | |
| | 7.1034 | 5150 | 0.4994 | |
| | 7.1724 | 5200 | 0.5842 | |
| | 7.2414 | 5250 | 0.5317 | |
| | 7.3103 | 5300 | 0.5112 | |
| | 7.3793 | 5350 | 0.5188 | |
| | 7.4483 | 5400 | 0.6174 | |
| | 7.5172 | 5450 | 0.484 | |
| | 7.5862 | 5500 | 0.5571 | |
| | 7.6552 | 5550 | 0.5043 | |
| | 7.7241 | 5600 | 0.5341 | |
| | 7.7931 | 5650 | 0.5225 | |
| | 7.8621 | 5700 | 0.5618 | |
| | 7.9310 | 5750 | 0.5537 | |
| | 8.0 | 5800 | 0.5811 | |
| | 8.0690 | 5850 | 0.5311 | |
| | 8.1379 | 5900 | 0.5585 | |
| | 8.2069 | 5950 | 0.5564 | |
| | 8.2759 | 6000 | 0.5469 | |
| | 8.3448 | 6050 | 0.5726 | |
| | 8.4138 | 6100 | 0.5329 | |
| | 8.4828 | 6150 | 0.55 | |
| | 8.5517 | 6200 | 0.5365 | |
| | 8.6207 | 6250 | 0.5847 | |
| | 8.6897 | 6300 | 0.5204 | |
| | 8.7586 | 6350 | 0.5112 | |
| | 8.8276 | 6400 | 0.5468 | |
| | 8.8966 | 6450 | 0.4871 | |
| | 8.9655 | 6500 | 0.5449 | |
| | 9.0345 | 6550 | 0.5237 | |
| | 9.1034 | 6600 | 0.5232 | |
| | 9.1724 | 6650 | 0.5075 | |
| | 9.2414 | 6700 | 0.5078 | |
| | 9.3103 | 6750 | 0.5366 | |
| | 9.3793 | 6800 | 0.5636 | |
| | 9.4483 | 6850 | 0.4743 | |
| | 9.5172 | 6900 | 0.4776 | |
| | 9.5862 | 6950 | 0.5571 | |
| | 9.6552 | 7000 | 0.56 | |
| | 9.7241 | 7050 | 0.5054 | |
| | 9.7931 | 7100 | 0.5431 | |
| | 9.8621 | 7150 | 0.5358 | |
| | 9.9310 | 7200 | 0.5395 | |
| | 10.0 | 7250 | 0.5394 | |
| | 10.0690 | 7300 | 0.57 | |
| | 10.1379 | 7350 | 0.4883 | |
| | 10.2069 | 7400 | 0.4884 | |
| | 10.2759 | 7450 | 0.4587 | |
| | 10.3448 | 7500 | 0.5076 | |
| | 10.4138 | 7550 | 0.5108 | |
| | 10.4828 | 7600 | 0.565 | |
| | 10.5517 | 7650 | 0.503 | |
| | 10.6207 | 7700 | 0.5645 | |
| | 10.6897 | 7750 | 0.509 | |
| | 10.7586 | 7800 | 0.4993 | |
| | 10.8276 | 7850 | 0.5464 | |
| | 10.8966 | 7900 | 0.5293 | |
| | 10.9655 | 7950 | 0.5384 | |
| | 11.0345 | 8000 | 0.5245 | |
| | 11.1034 | 8050 | 0.4647 | |
| | 11.1724 | 8100 | 0.4983 | |
| | 11.2414 | 8150 | 0.5168 | |
| | 11.3103 | 8200 | 0.5455 | |
| | 11.3793 | 8250 | 0.5069 | |
| | 11.4483 | 8300 | 0.5523 | |
| | 11.5172 | 8350 | 0.4875 | |
| | 11.5862 | 8400 | 0.4947 | |
| | 11.6552 | 8450 | 0.5022 | |
| | 11.7241 | 8500 | 0.5096 | |
| | 11.7931 | 8550 | 0.5768 | |
| | 11.8621 | 8600 | 0.5187 | |
| | 11.9310 | 8650 | 0.4883 | |
| | 12.0 | 8700 | 0.5039 | |
| | 12.0690 | 8750 | 0.527 | |
| | 12.1379 | 8800 | 0.5382 | |
| | 12.2069 | 8850 | 0.4912 | |
| | 12.2759 | 8900 | 0.5144 | |
| | 12.3448 | 8950 | 0.532 | |
| | 12.4138 | 9000 | 0.5233 | |
| | 12.4828 | 9050 | 0.4169 | |
| | 12.5517 | 9100 | 0.5278 | |
| | 12.6207 | 9150 | 0.5028 | |
| | 12.6897 | 9200 | 0.5227 | |
| | 12.7586 | 9250 | 0.4812 | |
| | 12.8276 | 9300 | 0.5299 | |
| | 12.8966 | 9350 | 0.5383 | |
| | 12.9655 | 9400 | 0.5245 | |
| | 13.0345 | 9450 | 0.5045 | |
| | 13.1034 | 9500 | 0.5619 | |
| | 13.1724 | 9550 | 0.4969 | |
| | 13.2414 | 9600 | 0.508 | |
| | 13.3103 | 9650 | 0.5095 | |
| | 13.3793 | 9700 | 0.5095 | |
| | 13.4483 | 9750 | 0.4886 | |
| | 13.5172 | 9800 | 0.5074 | |
| | 13.5862 | 9850 | 0.4761 | |
| | 13.6552 | 9900 | 0.4805 | |
| | 13.7241 | 9950 | 0.4559 | |
| | 13.7931 | 10000 | 0.5212 | |
| | 13.8621 | 10050 | 0.506 | |
| | 13.9310 | 10100 | 0.5086 | |
| | 14.0 | 10150 | 0.5232 | |
| | 14.0690 | 10200 | 0.5156 | |
| | 14.1379 | 10250 | 0.495 | |
| | 14.2069 | 10300 | 0.5226 | |
| | 14.2759 | 10350 | 0.4842 | |
| | 14.3448 | 10400 | 0.4514 | |
| | 14.4138 | 10450 | 0.4902 | |
| | 14.4828 | 10500 | 0.5068 | |
| | 14.5517 | 10550 | 0.5784 | |
| | 14.6207 | 10600 | 0.5646 | |
| | 14.6897 | 10650 | 0.4994 | |
| | 14.7586 | 10700 | 0.552 | |
| | 14.8276 | 10750 | 0.5216 | |
| | 14.8966 | 10800 | 0.5506 | |
| | 14.9655 | 10850 | 0.4286 | |
|
|
| </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} |
| } |
| ``` |
|
|
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