v4 two-pass masked kNN

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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
+}

README.md

@@ -0,0 +1,608 @@
+---
+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}
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.6",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.57.6",
+    "pytorch": "2.10.0+cu128"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9de14d88a88d33416cc45cd99cf263e917d3587407b92dbbee20036a61bc465
+size 90864192

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 256,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}