Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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 +1,670 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- loss:CosineSimilarityLoss
+base_model: Snowflake/snowflake-arctic-embed-l-v2.0
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l-v2.0
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) on the json dataset. It maps sentences & paragraphs to a 1024-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:** [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) <!-- at revision 7f311bb640ad3babc0a4e3a8873240dcba44c9d2 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+<!-- - **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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("LucaZilli/arctic-l-enhanced")
+# Run inference
+sentences = [
+    'The weather is lovely today.',
+    "It's so sunny outside!",
+    'He drove to the stadium.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## 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
+
+#### json
+
+* Dataset: json
+* Columns: <code>sentence1</code>, <code>sentence2</code>, <code>score</code>, and <code>split</code>
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### json
+
+* Dataset: json
+* Columns: <code>sentence1</code>, <code>sentence2</code>, <code>score</code>, and <code>split</code>
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 12
+- `per_device_eval_batch_size`: 12
+- `learning_rate`: 4.000000000000001e-06
+- `max_steps`: 9291
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `load_best_model_at_end`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 12
+- `per_device_eval_batch_size`: 12
+- `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`: 4.000000000000001e-06
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: 9291
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: 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`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `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
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0011 | 10   | 0.1329        | -               |
+| 0.0022 | 20   | 0.1211        | -               |
+| 0.0032 | 30   | 0.1533        | -               |
+| 0.0043 | 40   | 0.1325        | -               |
+| 0.0054 | 50   | 0.1076        | -               |
+| 0.0065 | 60   | 0.1349        | -               |
+| 0.0075 | 70   | 0.1224        | -               |
+| 0.0086 | 80   | 0.1062        | -               |
+| 0.0097 | 90   | 0.1026        | -               |
+| 0.0108 | 100  | 0.0873        | -               |
+| 0.0118 | 110  | 0.0733        | -               |
+| 0.0129 | 120  | 0.0799        | -               |
+| 0.0140 | 130  | 0.0773        | -               |
+| 0.0151 | 140  | 0.0666        | -               |
+| 0.0161 | 150  | 0.069         | 0.0615          |
+| 0.0172 | 160  | 0.0639        | -               |
+| 0.0183 | 170  | 0.063         | -               |
+| 0.0194 | 180  | 0.0739        | -               |
+| 0.0204 | 190  | 0.0708        | -               |
+| 0.0215 | 200  | 0.0532        | -               |
+| 0.0226 | 210  | 0.0573        | -               |
+| 0.0237 | 220  | 0.0503        | -               |
+| 0.0248 | 230  | 0.0564        | -               |
+| 0.0258 | 240  | 0.0592        | -               |
+| 0.0269 | 250  | 0.0555        | -               |
+| 0.0280 | 260  | 0.0513        | -               |
+| 0.0291 | 270  | 0.055         | -               |
+| 0.0301 | 280  | 0.0522        | -               |
+| 0.0312 | 290  | 0.054         | -               |
+| 0.0323 | 300  | 0.0548        | 0.0531          |
+| 0.0334 | 310  | 0.0495        | -               |
+| 0.0344 | 320  | 0.047         | -               |
+| 0.0355 | 330  | 0.0551        | -               |
+| 0.0366 | 340  | 0.0534        | -               |
+| 0.0377 | 350  | 0.0492        | -               |
+| 0.0387 | 360  | 0.0584        | -               |
+| 0.0398 | 370  | 0.0452        | -               |
+| 0.0409 | 380  | 0.0572        | -               |
+| 0.0420 | 390  | 0.0423        | -               |
+| 0.0431 | 400  | 0.0533        | -               |
+| 0.0441 | 410  | 0.0445        | -               |
+| 0.0452 | 420  | 0.0513        | -               |
+| 0.0463 | 430  | 0.0446        | -               |
+| 0.0474 | 440  | 0.0412        | -               |
+| 0.0484 | 450  | 0.0456        | 0.0544          |
+| 0.0495 | 460  | 0.0401        | -               |
+| 0.0506 | 470  | 0.0392        | -               |
+| 0.0517 | 480  | 0.042         | -               |
+| 0.0527 | 490  | 0.0513        | -               |
+| 0.0538 | 500  | 0.0368        | -               |
+| 0.0549 | 510  | 0.043         | -               |
+| 0.0560 | 520  | 0.0418        | -               |
+| 0.0570 | 530  | 0.0419        | -               |
+| 0.0581 | 540  | 0.0377        | -               |
+| 0.0592 | 550  | 0.0354        | -               |
+| 0.0603 | 560  | 0.0358        | -               |
+| 0.0613 | 570  | 0.0474        | -               |
+| 0.0624 | 580  | 0.0384        | -               |
+| 0.0635 | 590  | 0.0411        | -               |
+| 0.0646 | 600  | 0.0417        | 0.0558          |
+| 0.0657 | 610  | 0.0389        | -               |
+| 0.0667 | 620  | 0.0418        | -               |
+| 0.0678 | 630  | 0.0391        | -               |
+| 0.0689 | 640  | 0.0354        | -               |
+| 0.0700 | 650  | 0.0428        | -               |
+| 0.0710 | 660  | 0.0453        | -               |
+| 0.0721 | 670  | 0.0333        | -               |
+| 0.0732 | 680  | 0.0466        | -               |
+| 0.0743 | 690  | 0.0406        | -               |
+| 0.0753 | 700  | 0.0378        | -               |
+| 0.0764 | 710  | 0.0399        | -               |
+| 0.0775 | 720  | 0.036         | -               |
+| 0.0786 | 730  | 0.0403        | -               |
+| 0.0796 | 740  | 0.0408        | -               |
+| 0.0807 | 750  | 0.0335        | 0.0531          |
+| 0.0818 | 760  | 0.0335        | -               |
+| 0.0829 | 770  | 0.0387        | -               |
+| 0.0840 | 780  | 0.035         | -               |
+| 0.0850 | 790  | 0.0351        | -               |
+| 0.0861 | 800  | 0.0407        | -               |
+| 0.0872 | 810  | 0.0371        | -               |
+| 0.0883 | 820  | 0.0387        | -               |
+| 0.0893 | 830  | 0.0365        | -               |
+| 0.0904 | 840  | 0.0395        | -               |
+| 0.0915 | 850  | 0.0403        | -               |
+| 0.0926 | 860  | 0.04          | -               |
+| 0.0936 | 870  | 0.0356        | -               |
+| 0.0947 | 880  | 0.0333        | -               |
+| 0.0958 | 890  | 0.0269        | -               |
+| 0.0969 | 900  | 0.0341        | 0.0455          |
+| 0.0979 | 910  | 0.0294        | -               |
+| 0.0990 | 920  | 0.0269        | -               |
+| 0.1001 | 930  | 0.0293        | -               |
+| 0.1012 | 940  | 0.034         | -               |
+| 0.1022 | 950  | 0.0288        | -               |
+| 0.1033 | 960  | 0.017         | -               |
+| 0.1044 | 970  | 0.0345        | -               |
+| 0.1055 | 980  | 0.0331        | -               |
+| 0.1066 | 990  | 0.0279        | -               |
+| 0.1076 | 1000 | 0.0255        | -               |
+| 0.1087 | 1010 | 0.0279        | -               |
+| 0.1098 | 1020 | 0.0232        | -               |
+| 0.1109 | 1030 | 0.0299        | -               |
+| 0.1119 | 1040 | 0.0268        | -               |
+| 0.1130 | 1050 | 0.0196        | 0.0468          |
+| 0.1141 | 1060 | 0.0235        | -               |
+| 0.1152 | 1070 | 0.0305        | -               |
+| 0.1162 | 1080 | 0.0429        | -               |
+| 0.1173 | 1090 | 0.043         | -               |
+| 0.1184 | 1100 | 0.0408        | -               |
+| 0.1195 | 1110 | 0.0387        | -               |
+| 0.1205 | 1120 | 0.0389        | -               |
+| 0.1216 | 1130 | 0.0452        | -               |
+| 0.1227 | 1140 | 0.0424        | -               |
+| 0.1238 | 1150 | 0.0388        | -               |
+| 0.1249 | 1160 | 0.0474        | -               |
+| 0.1259 | 1170 | 0.0303        | -               |
+| 0.1270 | 1180 | 0.0379        | -               |
+| 0.1281 | 1190 | 0.033         | -               |
+| 0.1292 | 1200 | 0.0303        | 0.0361          |
+| 0.1302 | 1210 | 0.0361        | -               |
+| 0.1313 | 1220 | 0.0366        | -               |
+| 0.1324 | 1230 | 0.0359        | -               |
+| 0.1335 | 1240 | 0.0304        | -               |
+| 0.1345 | 1250 | 0.0265        | -               |
+| 0.1356 | 1260 | 0.0286        | -               |
+| 0.1367 | 1270 | 0.0326        | -               |
+| 0.1378 | 1280 | 0.0324        | -               |
+| 0.1388 | 1290 | 0.0304        | -               |
+| 0.1399 | 1300 | 0.0328        | -               |
+| 0.1410 | 1310 | 0.0339        | -               |
+| 0.1421 | 1320 | 0.0362        | -               |
+| 0.1431 | 1330 | 0.0318        | -               |
+| 0.1442 | 1340 | 0.0291        | -               |
+| 0.1453 | 1350 | 0.0241        | 0.0345          |
+| 0.1464 | 1360 | 0.0233        | -               |
+| 0.1475 | 1370 | 0.029         | -               |
+| 0.1485 | 1380 | 0.0224        | -               |
+| 0.1496 | 1390 | 0.0364        | -               |
+| 0.1507 | 1400 | 0.033         | -               |
+| 0.1518 | 1410 | 0.0337        | -               |
+| 0.1528 | 1420 | 0.0328        | -               |
+| 0.1539 | 1430 | 0.0253        | -               |
+| 0.1550 | 1440 | 0.028         | -               |
+| 0.1561 | 1450 | 0.023         | -               |
+| 0.1571 | 1460 | 0.034         | -               |
+| 0.1582 | 1470 | 0.0296        | -               |
+| 0.1593 | 1480 | 0.0278        | -               |
+| 0.1604 | 1490 | 0.0357        | -               |
+| 0.1614 | 1500 | 0.0267        | 0.0357          |
+| 0.1625 | 1510 | 0.0372        | -               |
+| 0.1636 | 1520 | 0.0264        | -               |
+| 0.1647 | 1530 | 0.0239        | -               |
+| 0.1658 | 1540 | 0.0307        | -               |
+| 0.1668 | 1550 | 0.0288        | -               |
+| 0.1679 | 1560 | 0.0275        | -               |
+| 0.1690 | 1570 | 0.0228        | -               |
+| 0.1701 | 1580 | 0.0219        | -               |
+| 0.1711 | 1590 | 0.0243        | -               |
+| 0.1722 | 1600 | 0.0191        | -               |
+| 0.1733 | 1610 | 0.018         | -               |
+| 0.1744 | 1620 | 0.0226        | -               |
+| 0.1754 | 1630 | 0.0261        | -               |
+| 0.1765 | 1640 | 0.0248        | -               |
+| 0.1776 | 1650 | 0.0199        | 0.0359          |
+| 0.1787 | 1660 | 0.0309        | -               |
+| 0.1797 | 1670 | 0.0213        | -               |
+| 0.1808 | 1680 | 0.0221        | -               |
+| 0.1819 | 1690 | 0.0257        | -               |
+| 0.1830 | 1700 | 0.0219        | -               |
+| 0.1840 | 1710 | 0.0294        | -               |
+| 0.1851 | 1720 | 0.021         | -               |
+| 0.1862 | 1730 | 0.0215        | -               |
+| 0.1873 | 1740 | 0.0187        | -               |
+| 0.1884 | 1750 | 0.021         | -               |
+| 0.1894 | 1760 | 0.02          | -               |
+| 0.1905 | 1770 | 0.0208        | -               |
+| 0.1916 | 1780 | 0.0184        | -               |
+| 0.1927 | 1790 | 0.0182        | -               |
+| 0.1937 | 1800 | 0.0158        | 0.0398          |
+| 0.1948 | 1810 | 0.0191        | -               |
+| 0.1959 | 1820 | 0.0256        | -               |
+| 0.1970 | 1830 | 0.0199        | -               |
+| 0.1980 | 1840 | 0.0163        | -               |
+| 0.1991 | 1850 | 0.0241        | -               |
+| 0.2002 | 1860 | 0.0153        | -               |
+| 0.2013 | 1870 | 0.0198        | -               |
+| 0.2023 | 1880 | 0.0177        | -               |
+| 0.2034 | 1890 | 0.0172        | -               |
+| 0.2045 | 1900 | 0.0154        | -               |
+| 0.2056 | 1910 | 0.0213        | -               |
+| 0.2067 | 1920 | 0.0159        | -               |
+| 0.2077 | 1930 | 0.0227        | -               |
+| 0.2088 | 1940 | 0.0149        | -               |
+| 0.2099 | 1950 | 0.0198        | 0.0423          |
+| 0.2110 | 1960 | 0.0178        | -               |
+| 0.2120 | 1970 | 0.0153        | -               |
+| 0.2131 | 1980 | 0.0163        | -               |
+| 0.2142 | 1990 | 0.0161        | -               |
+| 0.2153 | 2000 | 0.014         | -               |
+| 0.2163 | 2010 | 0.0143        | -               |
+| 0.2174 | 2020 | 0.0188        | -               |
+| 0.2185 | 2030 | 0.0159        | -               |
+| 0.2196 | 2040 | 0.0189        | -               |
+| 0.2206 | 2050 | 0.02          | -               |
+| 0.2217 | 2060 | 0.0152        | -               |
+| 0.2228 | 2070 | 0.0227        | -               |
+| 0.2239 | 2080 | 0.0194        | -               |
+| 0.2249 | 2090 | 0.0156        | -               |
+| 0.2260 | 2100 | 0.0159        | 0.0449          |
+| 0.2271 | 2110 | 0.0156        | -               |
+| 0.2282 | 2120 | 0.0152        | -               |
+| 0.2293 | 2130 | 0.016         | -               |
+| 0.2303 | 2140 | 0.0124        | -               |
+| 0.2314 | 2150 | 0.0157        | -               |
+| 0.2325 | 2160 | 0.0217        | -               |
+| 0.2336 | 2170 | 0.0146        | -               |
+| 0.2346 | 2180 | 0.015         | -               |
+| 0.2357 | 2190 | 0.0139        | -               |
+| 0.2368 | 2200 | 0.0139        | -               |
+| 0.2379 | 2210 | 0.0181        | -               |
+| 0.2389 | 2220 | 0.0196        | -               |
+| 0.2400 | 2230 | 0.0163        | -               |
+| 0.2411 | 2240 | 0.014         | -               |
+| 0.2422 | 2250 | 0.015         | 0.0469          |
+| 0.2432 | 2260 | 0.0156        | -               |
+| 0.2443 | 2270 | 0.0172        | -               |
+| 0.2454 | 2280 | 0.016         | -               |
+| 0.2465 | 2290 | 0.015         | -               |
+| 0.2476 | 2300 | 0.0171        | -               |
+| 0.2486 | 2310 | 0.0151        | -               |
+| 0.2497 | 2320 | 0.0147        | -               |
+| 0.2508 | 2330 | 0.0197        | -               |
+| 0.2519 | 2340 | 0.0153        | -               |
+| 0.2529 | 2350 | 0.0145        | -               |
+| 0.2540 | 2360 | 0.0143        | -               |
+| 0.2551 | 2370 | 0.0122        | -               |
+| 0.2562 | 2380 | 0.0151        | -               |
+| 0.2572 | 2390 | 0.0143        | -               |
+| 0.2583 | 2400 | 0.0136        | 0.0502          |
+| 0.2594 | 2410 | 0.0137        | -               |
+| 0.2605 | 2420 | 0.0143        | -               |
+| 0.2615 | 2430 | 0.0153        | -               |
+| 0.2626 | 2440 | 0.019         | -               |
+| 0.2637 | 2450 | 0.0125        | -               |
+| 0.2648 | 2460 | 0.0146        | -               |
+| 0.2658 | 2470 | 0.0154        | -               |
+| 0.2669 | 2480 | 0.0158        | -               |
+| 0.2680 | 2490 | 0.0129        | -               |
+| 0.2691 | 2500 | 0.0131        | -               |
+| 0.2702 | 2510 | 0.0217        | -               |
+| 0.2712 | 2520 | 0.0132        | -               |
+| 0.2723 | 2530 | 0.0133        | -               |
+| 0.2734 | 2540 | 0.0146        | -               |
+| 0.2745 | 2550 | 0.0152        | 0.0555          |
+| 0.2755 | 2560 | 0.014         | -               |
+| 0.2766 | 2570 | 0.0174        | -               |
+| 0.2777 | 2580 | 0.0161        | -               |
+| 0.2788 | 2590 | 0.0145        | -               |
+| 0.2798 | 2600 | 0.0193        | -               |
+| 0.2809 | 2610 | 0.0145        | -               |
+| 0.2820 | 2620 | 0.0146        | -               |
+| 0.2831 | 2630 | 0.0129        | -               |
+| 0.2841 | 2640 | 0.0158        | -               |
+| 0.2852 | 2650 | 0.0165        | -               |
+| 0.2863 | 2660 | 0.0135        | -               |
+| 0.2874 | 2670 | 0.0163        | -               |
+| 0.2885 | 2680 | 0.0159        | -               |
+| 0.2895 | 2690 | 0.0146        | -               |
+| 0.2906 | 2700 | 0.0186        | 0.0531          |
+| 0.2917 | 2710 | 0.0161        | -               |
+| 0.2928 | 2720 | 0.0149        | -               |
+| 0.2938 | 2730 | 0.0147        | -               |
+| 0.2949 | 2740 | 0.0128        | -               |
+| 0.2960 | 2750 | 0.0198        | -               |
+| 0.2971 | 2760 | 0.0123        | -               |
+| 0.2981 | 2770 | 0.0133        | -               |
+| 0.2992 | 2780 | 0.0146        | -               |
+| 0.3003 | 2790 | 0.0133        | -               |
+| 0.3014 | 2800 | 0.0158        | -               |
+| 0.3024 | 2810 | 0.0125        | -               |
+| 0.3035 | 2820 | 0.0122        | -               |
+| 0.3046 | 2830 | 0.0129        | -               |
+| 0.3057 | 2840 | 0.0132        | -               |
+| 0.3067 | 2850 | 0.0138        | 0.0472          |
+| 0.3078 | 2860 | 0.0134        | -               |
+| 0.3089 | 2870 | 0.0142        | -               |
+| 0.3100 | 2880 | 0.0141        | -               |
+| 0.3111 | 2890 | 0.019         | -               |
+| 0.3121 | 2900 | 0.0127        | -               |
+| 0.3132 | 2910 | 0.0117        | -               |
+| 0.3143 | 2920 | 0.0166        | -               |
+| 0.3154 | 2930 | 0.0365        | -               |
+| 0.3164 | 2940 | 0.0328        | -               |
+| 0.3175 | 2950 | 0.0344        | -               |
+| 0.3186 | 2960 | 0.0345        | -               |
+| 0.3197 | 2970 | 0.0312        | -               |
+| 0.3207 | 2980 | 0.017         | -               |
+| 0.3218 | 2990 | 0.0176        | -               |
+| 0.3229 | 3000 | 0.0145        | 0.0400          |
+| 0.3240 | 3010 | 0.0116        | -               |
+| 0.3250 | 3020 | 0.018         | -               |
+| 0.3261 | 3030 | 0.017         | -               |
+| 0.3272 | 3040 | 0.0114        | -               |
+| 0.3283 | 3050 | 0.0124        | -               |
+| 0.3294 | 3060 | 0.012         | -               |
+| 0.3304 | 3070 | 0.0118        | -               |
+| 0.3315 | 3080 | 0.01          | -               |
+| 0.3326 | 3090 | 0.0147        | -               |
+| 1.0002 | 3100 | 0.0212        | -               |
+| 1.0013 | 3110 | 0.0488        | -               |
+| 1.0024 | 3120 | 0.0495        | -               |
+| 1.0034 | 3130 | 0.0384        | -               |
+| 1.0045 | 3140 | 0.0422        | -               |
+| 1.0056 | 3150 | 0.0326        | 0.0453          |
+| 1.0067 | 3160 | 0.0375        | -               |
+| 1.0077 | 3170 | 0.0397        | -               |
+| 1.0088 | 3180 | 0.0469        | -               |
+| 1.0099 | 3190 | 0.0462        | -               |
+| 1.0110 | 3200 | 0.034         | -               |
+| 1.0121 | 3210 | 0.048         | -               |
+| 1.0131 | 3220 | 0.0377        | -               |
+| 1.0142 | 3230 | 0.0299        | -               |
+| 1.0153 | 3240 | 0.0344        | -               |
+| 1.0164 | 3250 | 0.04          | -               |
+| 1.0174 | 3260 | 0.0399        | -               |
+| 1.0185 | 3270 | 0.037         | -               |
+| 1.0196 | 3280 | 0.0365        | -               |
+| 1.0207 | 3290 | 0.039         | -               |
+| 1.0217 | 3300 | 0.0355        | 0.0462          |
+| 1.0228 | 3310 | 0.0328        | -               |
+| 1.0239 | 3320 | 0.0297        | -               |
+| 1.0250 | 3330 | 0.031         | -               |
+| 1.0260 | 3340 | 0.0387        | -               |
+| 1.0271 | 3350 | 0.0297        | -               |
+| 1.0282 | 3360 | 0.0355        | -               |
+| 1.0293 | 3370 | 0.0399        | -               |
+| 1.0304 | 3380 | 0.0321        | -               |
+| 1.0314 | 3390 | 0.0265        | -               |
+| 1.0325 | 3400 | 0.0345        | -               |
+| 1.0336 | 3410 | 0.0276        | -               |
+| 1.0347 | 3420 | 0.036         | -               |
+| 1.0357 | 3430 | 0.0295        | -               |
+| 1.0368 | 3440 | 0.036         | -               |
+| 1.0379 | 3450 | 0.032         | 0.0434          |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.4.1
+- Transformers: 4.49.0
+- PyTorch: 2.2.2
+- Accelerate: 1.4.0
+- Datasets: 3.3.2
+- Tokenizers: 0.21.0
+
+## 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",
+}
+```
+
+<!--
+## 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 +1,28 @@
+{
+  "_name_or_path": "arctic-l-wopenai-enhanced/checkpoint-3450",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,12 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.49.0",
+    "pytorch": "2.2.2"
+  },
+  "prompts": {
+    "query": "query: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1804d535a0b55ed7fe09ef40a24b9a4bb740e310dfab81506251d2d9863ef33e
+size 2271064456

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": 8192,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4f7e21bec3fb0044ca0bb2d50eb5d4d8c596273c422baef84466d2c73748b9c
+size 17083053

tokenizer_config.json

@@ -0,0 +1,62 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 8192,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizerFast",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}