Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 768,
+    "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

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:188228
+- loss:SoftmaxLoss
+base_model: google-bert/bert-base-cased
+widget:
+- source_sentence: COc1cc([C@@H]2Oc3c(O)cc([C@H]4Oc5cc(O)cc(O)c5C(=O)[C@@H]4O)cc3[C@H]2CO)ccc1O
+  sentences:
+  - CCCCCCCCCCCCCCC(=O)OCC(O)CO
+  - COc1cc(-c2ccc(C(=O)NCc3ccccc3)c(O)c2)cc(OC)c1O
+  - COc1cc(/C=C/C(=N\O)c2cc3ccccc3cc2O)cc(OC)c1
+- source_sentence: CN1[C@@H]2CC[C@H]1C/C(=N/Nc1nc(-c3ccc(Cl)cc3Cl)cs1)C2
+  sentences:
+  - CCC(=O)N1CCN(Cc2ccc(F)cc2)CC1
+  - Nc1ccccc1C(=O)N1CCN(Cc2ccc(F)cc2)CC1
+  - O=C(c1cccc(O)c1)N1CCN(Cc2ccc(F)cc2)CC1
+- source_sentence: Cc1ccc(C(=O)Nc2cccc(C(=O)/C=C/c3ccc4c(c3)c3ccccc3n4C)c2)cc1
+  sentences:
+  - O=C(/C=C/c1ccc(O)c(O)c1)NC(Cc1ccccc1)C(=O)NO
+  - COc1cccc(OC(=O)COC(=O)c2ccc(O)cc2O)c1
+  - NC(=S)N/N=C/c1cccc2ccccc12
+- source_sentence: CC(=O)c1c(O)ccc(C(c2ccc(O)cc2O)c2ccc(O)c(C(C)=O)c2O)c1O
+  sentences:
+  - C=CC(C)(C)c1cc(C2CC(=O)c3c(cc(O)c(CC=C(C)C)c3O)O2)c(OC)cc1O
+  - O=C1ON=C(c2ccccc2)/C1=C/c1ccc(O)cc1O
+  - COc1cc(-c2ccc(C(=O)O)c(O)c2)cc(OC)c1O
+- source_sentence: CCCCc1ccc(/C(CC)=N/NC(N)=S)cc1
+  sentences:
+  - COc1ccccc1/C=N/NC(=O)c1ccc(OC)c(OC)c1
+  - COc1cc([C@@H]2Oc3c(O)cc([C@H]4Oc5cc(O)cc(O)c5C(=O)[C@@H]4O)cc3[C@H]2CO)ccc1O
+  - O=C(/C=C/c1ccc(O)c(O)c1)NCCc1c[nH]c2ccc(O)cc12
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on google-bert/bert-base-cased
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the csv dataset. It maps sentences & paragraphs to a 768-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:** [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) <!-- at revision cd5ef92a9fb2f889e972770a36d4ed042daf221e -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - csv
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
+  (1): Pooling({'word_embedding_dimension': 768, '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})
+)
+```
+
+## 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("Jimmy-Ooi/Tyrisonase_test_model_1000_10_adafactor")
+# Run inference
+sentences = [
+    'CCCCc1ccc(/C(CC)=N/NC(N)=S)cc1',
+    'COc1ccccc1/C=N/NC(=O)c1ccc(OC)c(OC)c1',
+    'O=C(/C=C/c1ccc(O)c(O)c1)NCCc1c[nH]c2ccc(O)cc12',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 1.0000, -0.2733, -0.0760],
+#         [-0.2733,  1.0000,  0.9683],
+#         [-0.0760,  0.9683,  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
+
+#### csv
+
+* Dataset: csv
+* Size: 188,228 training samples
+* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | premise                                                                            | hypothesis                                                                         | label                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | int                                             |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 38.76 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 39.72 tokens</li><li>max: 145 tokens</li></ul> | <ul><li>0: ~50.20%</li><li>2: ~49.80%</li></ul> |
+* Samples:
+  | premise                                                              | hypothesis                                                                | label          |
+  |:---------------------------------------------------------------------|:--------------------------------------------------------------------------|:---------------|
+  | <code>COc1cc(OC)c(C2CCN(C)C2CO)c(O)c1-c1cc(-c2ccccc2Cl)[nH]n1</code> | <code>O=C(O)c1ccc(OCc2cn(Cc3cc(=O)c(O)co3)nn2)cc1</code>                  | <code>2</code> |
+  | <code>Cl.NC(Cc1ccc(=O)n(O)c1)C(=O)O</code>                           | <code>Cn1c2ccccc2c2cc(/C=C/C(=O)c3cccc(NC(=O)c4cccc(Cl)c4)c3)ccc21</code> | <code>0</code> |
+  | <code>Cc1ccc(O)cc1O</code>                                           | <code>O=C1NC(=O)C(=Cc2cc(O)c(O)c(O)c2)C(=O)N1</code>                      | <code>0</code> |
+* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
+
+### Evaluation Dataset
+
+#### csv
+
+* Dataset: csv
+* Size: 33,217 evaluation samples
+* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | premise                                                                             | hypothesis                                                                         | label                                           |
+  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                              | string                                                                             | int                                             |
+  | details | <ul><li>min: 11 tokens</li><li>mean: 39.05 tokens</li><li>max: 145 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 39.29 tokens</li><li>max: 145 tokens</li></ul> | <ul><li>0: ~51.40%</li><li>2: ~48.60%</li></ul> |
+* Samples:
+  | premise                                                                                       | hypothesis                                                    | label          |
+  |:----------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:---------------|
+  | <code>CC(=O)Oc1c(/N=N/c2ccc(C3=N/C(=C/c4ccc(OC(F)(F)F)cc4)C(=O)O3)cc2)ccc2ccccc12</code>      | <code>CC1(C)C=Cc2c(cc3c(c2O)C(=O)CC(c2ccc(O)cc2O)O3)O1</code> | <code>0</code> |
+  | <code>COc1cc2c(c(OC)c1OC)[C@@H]1O[C@H](COC(=O)c3ccc(O)cc3)[C@@H](O)[C@H](O)[C@H]1OC2=O</code> | <code>O=C(c1cccc(F)c1)N1CCN(Cc2ccc(F)cc2)CC1</code>           | <code>0</code> |
+  | <code>CC(=O)Nc1ccc(/N=N/c2c(N)nc(N)nc2Cl)cc1</code>                                           | <code>NC(=S)c1ccncc1</code>                                   | <code>0</code> |
+* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `weight_decay`: 0.001
+- `num_train_epochs`: 10
+- `warmup_steps`: 100
+- `fp16`: True
+- `optim`: adafactor
+
+#### 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`: 64
+- `per_device_eval_batch_size`: 64
+- `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`: 5e-05
+- `weight_decay`: 0.001
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 100
+- `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`: adafactor
+- `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 |
+|:------:|:-----:|:-------------:|
+| 0.0340 | 100   | 0.7569        |
+| 0.0680 | 200   | 0.6817        |
+| 0.1020 | 300   | 0.6559        |
+| 0.1360 | 400   | 0.6302        |
+| 0.1700 | 500   | 0.6204        |
+| 0.2039 | 600   | 0.6015        |
+| 0.2379 | 700   | 0.5901        |
+| 0.2719 | 800   | 0.583         |
+| 0.3059 | 900   | 0.5892        |
+| 0.3399 | 1000  | 0.58          |
+| 0.3739 | 1100  | 0.5752        |
+| 0.4079 | 1200  | 0.5707        |
+| 0.4419 | 1300  | 0.5727        |
+| 0.4759 | 1400  | 0.5562        |
+| 0.5099 | 1500  | 0.5736        |
+| 0.5438 | 1600  | 0.5609        |
+| 0.5778 | 1700  | 0.5545        |
+| 0.6118 | 1800  | 0.5528        |
+| 0.6458 | 1900  | 0.5503        |
+| 0.6798 | 2000  | 0.5527        |
+| 0.7138 | 2100  | 0.5552        |
+| 0.7478 | 2200  | 0.5499        |
+| 0.7818 | 2300  | 0.5477        |
+| 0.8158 | 2400  | 0.5429        |
+| 0.8498 | 2500  | 0.5314        |
+| 0.8838 | 2600  | 0.5542        |
+| 0.9177 | 2700  | 0.5373        |
+| 0.9517 | 2800  | 0.5321        |
+| 0.9857 | 2900  | 0.5412        |
+| 1.0197 | 3000  | 0.5367        |
+| 1.0537 | 3100  | 0.5368        |
+| 1.0877 | 3200  | 0.5388        |
+| 1.1217 | 3300  | 0.5419        |
+| 1.1557 | 3400  | 0.5303        |
+| 1.1897 | 3500  | 0.5369        |
+| 1.2237 | 3600  | 0.5357        |
+| 1.2576 | 3700  | 0.5296        |
+| 1.2916 | 3800  | 0.5368        |
+| 1.3256 | 3900  | 0.5351        |
+| 1.3596 | 4000  | 0.533         |
+| 1.3936 | 4100  | 0.5294        |
+| 1.4276 | 4200  | 0.5341        |
+| 1.4616 | 4300  | 0.5307        |
+| 1.4956 | 4400  | 0.5295        |
+| 1.5296 | 4500  | 0.5269        |
+| 1.5636 | 4600  | 0.5272        |
+| 1.5976 | 4700  | 0.5227        |
+| 1.6315 | 4800  | 0.529         |
+| 1.6655 | 4900  | 0.5316        |
+| 1.6995 | 5000  | 0.53          |
+| 1.7335 | 5100  | 0.5251        |
+| 1.7675 | 5200  | 0.5294        |
+| 1.8015 | 5300  | 0.5225        |
+| 1.8355 | 5400  | 0.5204        |
+| 1.8695 | 5500  | 0.5139        |
+| 1.9035 | 5600  | 0.525         |
+| 1.9375 | 5700  | 0.5242        |
+| 1.9714 | 5800  | 0.5208        |
+| 2.0054 | 5900  | 0.5183        |
+| 2.0394 | 6000  | 0.523         |
+| 2.0734 | 6100  | 0.5144        |
+| 2.1074 | 6200  | 0.514         |
+| 2.1414 | 6300  | 0.516         |
+| 2.1754 | 6400  | 0.527         |
+| 2.2094 | 6500  | 0.5182        |
+| 2.2434 | 6600  | 0.5213        |
+| 2.2774 | 6700  | 0.5162        |
+| 2.3114 | 6800  | 0.5202        |
+| 2.3453 | 6900  | 0.5258        |
+| 2.3793 | 7000  | 0.5191        |
+| 2.4133 | 7100  | 0.5185        |
+| 2.4473 | 7200  | 0.5134        |
+| 2.4813 | 7300  | 0.5231        |
+| 2.5153 | 7400  | 0.513         |
+| 2.5493 | 7500  | 0.5167        |
+| 2.5833 | 7600  | 0.5089        |
+| 2.6173 | 7700  | 0.5163        |
+| 2.6513 | 7800  | 0.517         |
+| 2.6852 | 7900  | 0.5081        |
+| 2.7192 | 8000  | 0.5171        |
+| 2.7532 | 8100  | 0.5138        |
+| 2.7872 | 8200  | 0.508         |
+| 2.8212 | 8300  | 0.5172        |
+| 2.8552 | 8400  | 0.5109        |
+| 2.8892 | 8500  | 0.5023        |
+| 2.9232 | 8600  | 0.5128        |
+| 2.9572 | 8700  | 0.5119        |
+| 2.9912 | 8800  | 0.5082        |
+| 3.0252 | 8900  | 0.5183        |
+| 3.0591 | 9000  | 0.512         |
+| 3.0931 | 9100  | 0.5112        |
+| 3.1271 | 9200  | 0.5157        |
+| 3.1611 | 9300  | 0.5066        |
+| 3.1951 | 9400  | 0.5035        |
+| 3.2291 | 9500  | 0.5037        |
+| 3.2631 | 9600  | 0.5112        |
+| 3.2971 | 9700  | 0.5147        |
+| 3.3311 | 9800  | 0.5112        |
+| 3.3651 | 9900  | 0.5           |
+| 3.3990 | 10000 | 0.5152        |
+| 3.4330 | 10100 | 0.5146        |
+| 3.4670 | 10200 | 0.5103        |
+| 3.5010 | 10300 | 0.5129        |
+| 3.5350 | 10400 | 0.5005        |
+| 3.5690 | 10500 | 0.5065        |
+| 3.6030 | 10600 | 0.5105        |
+| 3.6370 | 10700 | 0.5101        |
+| 3.6710 | 10800 | 0.5058        |
+| 3.7050 | 10900 | 0.5093        |
+| 3.7390 | 11000 | 0.5102        |
+| 3.7729 | 11100 | 0.511         |
+| 3.8069 | 11200 | 0.4982        |
+| 3.8409 | 11300 | 0.4973        |
+| 3.8749 | 11400 | 0.5068        |
+| 3.9089 | 11500 | 0.497         |
+| 3.9429 | 11600 | 0.5018        |
+| 3.9769 | 11700 | 0.5028        |
+| 4.0109 | 11800 | 0.5132        |
+| 4.0449 | 11900 | 0.5024        |
+| 4.0789 | 12000 | 0.4992        |
+| 4.1128 | 12100 | 0.4954        |
+| 4.1468 | 12200 | 0.5094        |
+| 4.1808 | 12300 | 0.5091        |
+| 4.2148 | 12400 | 0.507         |
+| 4.2488 | 12500 | 0.504         |
+| 4.2828 | 12600 | 0.5029        |
+| 4.3168 | 12700 | 0.4976        |
+| 4.3508 | 12800 | 0.5001        |
+| 4.3848 | 12900 | 0.5077        |
+| 4.4188 | 13000 | 0.496         |
+| 4.4528 | 13100 | 0.5075        |
+| 4.4867 | 13200 | 0.5059        |
+| 4.5207 | 13300 | 0.5111        |
+| 4.5547 | 13400 | 0.504         |
+| 4.5887 | 13500 | 0.4977        |
+| 4.6227 | 13600 | 0.5156        |
+| 4.6567 | 13700 | 0.4949        |
+| 4.6907 | 13800 | 0.5064        |
+| 4.7247 | 13900 | 0.5014        |
+| 4.7587 | 14000 | 0.5006        |
+| 4.7927 | 14100 | 0.5018        |
+| 4.8266 | 14200 | 0.5079        |
+| 4.8606 | 14300 | 0.5089        |
+| 4.8946 | 14400 | 0.5006        |
+| 4.9286 | 14500 | 0.5123        |
+| 4.9626 | 14600 | 0.5019        |
+| 4.9966 | 14700 | 0.5023        |
+| 5.0306 | 14800 | 0.496         |
+| 5.0646 | 14900 | 0.4934        |
+| 5.0986 | 15000 | 0.5006        |
+| 5.1326 | 15100 | 0.5021        |
+| 5.1666 | 15200 | 0.4989        |
+| 5.2005 | 15300 | 0.4932        |
+| 5.2345 | 15400 | 0.5023        |
+| 5.2685 | 15500 | 0.5047        |
+| 5.3025 | 15600 | 0.5007        |
+| 5.3365 | 15700 | 0.4982        |
+| 5.3705 | 15800 | 0.5005        |
+| 5.4045 | 15900 | 0.5101        |
+| 5.4385 | 16000 | 0.4958        |
+| 5.4725 | 16100 | 0.5039        |
+| 5.5065 | 16200 | 0.4988        |
+| 5.5404 | 16300 | 0.5028        |
+| 5.5744 | 16400 | 0.499         |
+| 5.6084 | 16500 | 0.4923        |
+| 5.6424 | 16600 | 0.5024        |
+| 5.6764 | 16700 | 0.5022        |
+| 5.7104 | 16800 | 0.5007        |
+| 5.7444 | 16900 | 0.4982        |
+| 5.7784 | 17000 | 0.4969        |
+| 5.8124 | 17100 | 0.4981        |
+| 5.8464 | 17200 | 0.4987        |
+| 5.8804 | 17300 | 0.4964        |
+| 5.9143 | 17400 | 0.4974        |
+| 5.9483 | 17500 | 0.4925        |
+| 5.9823 | 17600 | 0.5087        |
+| 6.0163 | 17700 | 0.4963        |
+| 6.0503 | 17800 | 0.4954        |
+| 6.0843 | 17900 | 0.4914        |
+| 6.1183 | 18000 | 0.4878        |
+| 6.1523 | 18100 | 0.5001        |
+| 6.1863 | 18200 | 0.5008        |
+| 6.2203 | 18300 | 0.5035        |
+| 6.2542 | 18400 | 0.5016        |
+| 6.2882 | 18500 | 0.4944        |
+| 6.3222 | 18600 | 0.5011        |
+| 6.3562 | 18700 | 0.4927        |
+| 6.3902 | 18800 | 0.4965        |
+| 6.4242 | 18900 | 0.5039        |
+| 6.4582 | 19000 | 0.4971        |
+| 6.4922 | 19100 | 0.4992        |
+| 6.5262 | 19200 | 0.488         |
+| 6.5602 | 19300 | 0.4935        |
+| 6.5942 | 19400 | 0.5032        |
+| 6.6281 | 19500 | 0.4955        |
+| 6.6621 | 19600 | 0.494         |
+| 6.6961 | 19700 | 0.4997        |
+| 6.7301 | 19800 | 0.4941        |
+| 6.7641 | 19900 | 0.4996        |
+| 6.7981 | 20000 | 0.4951        |
+| 6.8321 | 20100 | 0.497         |
+| 6.8661 | 20200 | 0.4989        |
+| 6.9001 | 20300 | 0.4937        |
+| 6.9341 | 20400 | 0.4983        |
+| 6.9680 | 20500 | 0.4968        |
+| 7.0020 | 20600 | 0.5024        |
+| 7.0360 | 20700 | 0.4979        |
+| 7.0700 | 20800 | 0.4919        |
+| 7.1040 | 20900 | 0.509         |
+| 7.1380 | 21000 | 0.4961        |
+| 7.1720 | 21100 | 0.4981        |
+| 7.2060 | 21200 | 0.4903        |
+| 7.2400 | 21300 | 0.4995        |
+| 7.2740 | 21400 | 0.4961        |
+| 7.3080 | 21500 | 0.4929        |
+| 7.3419 | 21600 | 0.4919        |
+| 7.3759 | 21700 | 0.5023        |
+| 7.4099 | 21800 | 0.4865        |
+| 7.4439 | 21900 | 0.4984        |
+| 7.4779 | 22000 | 0.4882        |
+| 7.5119 | 22100 | 0.4928        |
+| 7.5459 | 22200 | 0.4929        |
+| 7.5799 | 22300 | 0.504         |
+| 7.6139 | 22400 | 0.4998        |
+| 7.6479 | 22500 | 0.494         |
+| 7.6818 | 22600 | 0.4891        |
+| 7.7158 | 22700 | 0.4981        |
+| 7.7498 | 22800 | 0.4888        |
+| 7.7838 | 22900 | 0.4893        |
+| 7.8178 | 23000 | 0.4948        |
+| 7.8518 | 23100 | 0.4985        |
+| 7.8858 | 23200 | 0.5004        |
+| 7.9198 | 23300 | 0.492         |
+| 7.9538 | 23400 | 0.4937        |
+| 7.9878 | 23500 | 0.4947        |
+| 8.0218 | 23600 | 0.4932        |
+| 8.0557 | 23700 | 0.491         |
+| 8.0897 | 23800 | 0.4966        |
+| 8.1237 | 23900 | 0.5002        |
+| 8.1577 | 24000 | 0.4956        |
+| 8.1917 | 24100 | 0.4923        |
+| 8.2257 | 24200 | 0.4935        |
+| 8.2597 | 24300 | 0.492         |
+| 8.2937 | 24400 | 0.489         |
+| 8.3277 | 24500 | 0.4948        |
+| 8.3617 | 24600 | 0.4937        |
+| 8.3956 | 24700 | 0.4909        |
+| 8.4296 | 24800 | 0.5005        |
+| 8.4636 | 24900 | 0.4962        |
+| 8.4976 | 25000 | 0.4865        |
+| 8.5316 | 25100 | 0.4893        |
+| 8.5656 | 25200 | 0.4931        |
+| 8.5996 | 25300 | 0.4968        |
+| 8.6336 | 25400 | 0.4951        |
+| 8.6676 | 25500 | 0.4907        |
+| 8.7016 | 25600 | 0.505         |
+| 8.7356 | 25700 | 0.4938        |
+| 8.7695 | 25800 | 0.4953        |
+| 8.8035 | 25900 | 0.4968        |
+| 8.8375 | 26000 | 0.4854        |
+| 8.8715 | 26100 | 0.4847        |
+| 8.9055 | 26200 | 0.4918        |
+| 8.9395 | 26300 | 0.4987        |
+| 8.9735 | 26400 | 0.4918        |
+| 9.0075 | 26500 | 0.5023        |
+| 9.0415 | 26600 | 0.4976        |
+| 9.0755 | 26700 | 0.4947        |
+| 9.1094 | 26800 | 0.4924        |
+| 9.1434 | 26900 | 0.4914        |
+| 9.1774 | 27000 | 0.4976        |
+| 9.2114 | 27100 | 0.4908        |
+| 9.2454 | 27200 | 0.4873        |
+| 9.2794 | 27300 | 0.491         |
+| 9.3134 | 27400 | 0.4912        |
+| 9.3474 | 27500 | 0.4915        |
+| 9.3814 | 27600 | 0.4933        |
+| 9.4154 | 27700 | 0.4949        |
+| 9.4494 | 27800 | 0.4978        |
+| 9.4833 | 27900 | 0.4956        |
+| 9.5173 | 28000 | 0.4854        |
+| 9.5513 | 28100 | 0.4919        |
+| 9.5853 | 28200 | 0.4919        |
+| 9.6193 | 28300 | 0.4979        |
+| 9.6533 | 28400 | 0.4921        |
+| 9.6873 | 28500 | 0.4961        |
+| 9.7213 | 28600 | 0.4918        |
+| 9.7553 | 28700 | 0.4923        |
+| 9.7893 | 28800 | 0.4934        |
+| 9.8232 | 28900 | 0.4871        |
+| 9.8572 | 29000 | 0.4879        |
+| 9.8912 | 29100 | 0.4922        |
+| 9.9252 | 29200 | 0.4921        |
+| 9.9592 | 29300 | 0.4884        |
+| 9.9932 | 29400 | 0.4936        |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.1.2
+- Transformers: 4.57.3
+- PyTorch: 2.9.0+cu126
+- Accelerate: 1.12.0
+- Datasets: 4.0.0
+- Tokenizers: 0.22.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers and SoftmaxLoss
+```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",
+}
+```
+
+<!--
+## 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": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.57.3",
+    "pytorch": "2.9.0+cu126"
+  },
+  "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 sha256:2336f5315c5175f6951fb266b8efe4b57b83ba43afdd4f6f330d045a4cc7009e
+size 433263448

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "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_lower_case": false,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}