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checkpoint-21500/1_Pooling/config.json

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+{
+  "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
+}

checkpoint-21500/README.md

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+---
+base_model: sentence-transformers/all-MiniLM-L6-v2
+datasets:
+- youssefkhalil320/pairs_three_scores_v5
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:80000003
+- loss:CoSENTLoss
+widget:
+- source_sentence: durable pvc swim ring
+  sentences:
+  - flaky croissant
+  - urban shoes
+  - warm drinks mug
+- source_sentence: iso mak retard capsules
+  sentences:
+  - savory baguette
+  - shea butter body cream
+  - softwheeled cruiser
+- source_sentence: love sandra potty
+  sentences:
+  - utensil holder
+  - olive pants
+  - headwear
+- source_sentence: dusky hair brush
+  sentences:
+  - back compartment laptop
+  - rubber feet platter
+  - honed blade knife
+- source_sentence: nkd skn
+  sentences:
+  - fruit fragrances nail polish remover
+  - panini salmon
+  - hand drawing bag
+---
+
+# all-MiniLM-L6-v8-pair_score
+
+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) on the [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) dataset. 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 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5)
+- **Language:** en
+- **License:** apache-2.0
+
+### 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}) with Transformer model: 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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'nkd skn',
+    'hand drawing bag',
+    'panini salmon',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# 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
+
+#### pairs_three_scores_v5
+
+* Dataset: [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) at [3d8c457](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5/tree/3d8c45703846bd2adfaaf422abafbc389b283de1)
+* Size: 80,000,003 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                        | score                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | float                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 6.06 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.71 tokens</li><li>max: 13 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.11</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                            | sentence2                              | score            |
+  |:-------------------------------------|:---------------------------------------|:-----------------|
+  | <code>vanilla hair cream</code>      | <code>free of paraben hair mask</code> | <code>0.5</code> |
+  | <code>nourishing shampoo</code>      | <code>cumin lemon tea</code>           | <code>0.0</code> |
+  | <code>safe materials pacifier</code> | <code>facial serum</code>              | <code>0.5</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### pairs_three_scores_v5
+
+* Dataset: [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) at [3d8c457](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5/tree/3d8c45703846bd2adfaaf422abafbc389b283de1)
+* Size: 20,000,001 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                        | score                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | float                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 6.21 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.75 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.11</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                               | sentence2                          | score            |
+  |:----------------------------------------|:-----------------------------------|:-----------------|
+  | <code>teddy bear toy</code>             | <code>long lasting cat food</code> | <code>0.0</code> |
+  | <code>eva hair treatment</code>         | <code>fresh pineapple</code>       | <code>0.0</code> |
+  | <code>soft wave hair conditioner</code> | <code>hybrid seat bike</code>      | <code>0.0</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: 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`: 128
+- `per_device_eval_batch_size`: 128
+- `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`: 1
+- `max_steps`: -1
+- `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`: 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}
+- `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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0002 | 100   | 10.8792       |
+| 0.0003 | 200   | 10.9284       |
+| 0.0005 | 300   | 10.6466       |
+| 0.0006 | 400   | 10.841        |
+| 0.0008 | 500   | 10.8094       |
+| 0.0010 | 600   | 10.4323       |
+| 0.0011 | 700   | 10.3032       |
+| 0.0013 | 800   | 10.4006       |
+| 0.0014 | 900   | 10.4743       |
+| 0.0016 | 1000  | 10.2334       |
+| 0.0018 | 1100  | 10.0135       |
+| 0.0019 | 1200  | 9.7874        |
+| 0.0021 | 1300  | 9.7419        |
+| 0.0022 | 1400  | 9.7412        |
+| 0.0024 | 1500  | 9.4585        |
+| 0.0026 | 1600  | 9.5339        |
+| 0.0027 | 1700  | 9.4345        |
+| 0.0029 | 1800  | 9.1733        |
+| 0.0030 | 1900  | 8.9952        |
+| 0.0032 | 2000  | 8.9669        |
+| 0.0034 | 2100  | 8.8152        |
+| 0.0035 | 2200  | 8.7936        |
+| 0.0037 | 2300  | 8.6771        |
+| 0.0038 | 2400  | 8.4648        |
+| 0.0040 | 2500  | 8.5764        |
+| 0.0042 | 2600  | 8.4587        |
+| 0.0043 | 2700  | 8.2966        |
+| 0.0045 | 2800  | 8.2329        |
+| 0.0046 | 2900  | 8.1415        |
+| 0.0048 | 3000  | 8.0404        |
+| 0.0050 | 3100  | 7.9698        |
+| 0.0051 | 3200  | 7.9205        |
+| 0.0053 | 3300  | 7.8314        |
+| 0.0054 | 3400  | 7.8369        |
+| 0.0056 | 3500  | 7.6403        |
+| 0.0058 | 3600  | 7.5842        |
+| 0.0059 | 3700  | 7.5812        |
+| 0.0061 | 3800  | 7.4335        |
+| 0.0062 | 3900  | 7.4917        |
+| 0.0064 | 4000  | 7.3204        |
+| 0.0066 | 4100  | 7.2971        |
+| 0.0067 | 4200  | 7.2233        |
+| 0.0069 | 4300  | 7.2081        |
+| 0.0070 | 4400  | 7.1364        |
+| 0.0072 | 4500  | 7.0663        |
+| 0.0074 | 4600  | 6.9601        |
+| 0.0075 | 4700  | 6.9546        |
+| 0.0077 | 4800  | 6.9019        |
+| 0.0078 | 4900  | 6.8801        |
+| 0.0080 | 5000  | 6.7734        |
+| 0.0082 | 5100  | 6.7648        |
+| 0.0083 | 5200  | 6.7498        |
+| 0.0085 | 5300  | 6.6872        |
+| 0.0086 | 5400  | 6.6264        |
+| 0.0088 | 5500  | 6.579         |
+| 0.0090 | 5600  | 6.6001        |
+| 0.0091 | 5700  | 6.5971        |
+| 0.0093 | 5800  | 6.4694        |
+| 0.0094 | 5900  | 6.3983        |
+| 0.0096 | 6000  | 6.4477        |
+| 0.0098 | 6100  | 6.4308        |
+| 0.0099 | 6200  | 6.4248        |
+| 0.0101 | 6300  | 6.2642        |
+| 0.0102 | 6400  | 6.2763        |
+| 0.0104 | 6500  | 6.3878        |
+| 0.0106 | 6600  | 6.2601        |
+| 0.0107 | 6700  | 6.1789        |
+| 0.0109 | 6800  | 6.1773        |
+| 0.0110 | 6900  | 6.1439        |
+| 0.0112 | 7000  | 6.1863        |
+| 0.0114 | 7100  | 6.0513        |
+| 0.0115 | 7200  | 6.0671        |
+| 0.0117 | 7300  | 6.0212        |
+| 0.0118 | 7400  | 6.0043        |
+| 0.0120 | 7500  | 6.0166        |
+| 0.0122 | 7600  | 5.9754        |
+| 0.0123 | 7700  | 5.9211        |
+| 0.0125 | 7800  | 5.7867        |
+| 0.0126 | 7900  | 5.8534        |
+| 0.0128 | 8000  | 5.7708        |
+| 0.0130 | 8100  | 5.8328        |
+| 0.0131 | 8200  | 5.7417        |
+| 0.0133 | 8300  | 5.8097        |
+| 0.0134 | 8400  | 5.7578        |
+| 0.0136 | 8500  | 5.643         |
+| 0.0138 | 8600  | 5.6401        |
+| 0.0139 | 8700  | 5.6627        |
+| 0.0141 | 8800  | 5.6167        |
+| 0.0142 | 8900  | 5.6539        |
+| 0.0144 | 9000  | 5.4513        |
+| 0.0146 | 9100  | 5.4132        |
+| 0.0147 | 9200  | 5.4714        |
+| 0.0149 | 9300  | 5.4786        |
+| 0.0150 | 9400  | 5.3928        |
+| 0.0152 | 9500  | 5.4774        |
+| 0.0154 | 9600  | 5.2881        |
+| 0.0155 | 9700  | 5.3699        |
+| 0.0157 | 9800  | 5.1483        |
+| 0.0158 | 9900  | 5.3051        |
+| 0.0160 | 10000 | 5.2546        |
+| 0.0162 | 10100 | 5.2314        |
+| 0.0163 | 10200 | 5.1783        |
+| 0.0165 | 10300 | 5.2074        |
+| 0.0166 | 10400 | 5.2825        |
+| 0.0168 | 10500 | 5.1715        |
+| 0.0170 | 10600 | 5.087         |
+| 0.0171 | 10700 | 5.082         |
+| 0.0173 | 10800 | 4.9111        |
+| 0.0174 | 10900 | 5.0213        |
+| 0.0176 | 11000 | 4.9898        |
+| 0.0178 | 11100 | 4.7734        |
+| 0.0179 | 11200 | 4.9511        |
+| 0.0181 | 11300 | 5.0481        |
+| 0.0182 | 11400 | 4.8441        |
+| 0.0184 | 11500 | 4.873         |
+| 0.0186 | 11600 | 4.9988        |
+| 0.0187 | 11700 | 4.7653        |
+| 0.0189 | 11800 | 4.804         |
+| 0.0190 | 11900 | 4.8288        |
+| 0.0192 | 12000 | 4.7053        |
+| 0.0194 | 12100 | 4.6887        |
+| 0.0195 | 12200 | 4.7832        |
+| 0.0197 | 12300 | 4.6817        |
+| 0.0198 | 12400 | 4.6252        |
+| 0.0200 | 12500 | 4.5936        |
+| 0.0202 | 12600 | 4.7452        |
+| 0.0203 | 12700 | 4.5321        |
+| 0.0205 | 12800 | 4.4964        |
+| 0.0206 | 12900 | 4.4421        |
+| 0.0208 | 13000 | 4.3782        |
+| 0.0210 | 13100 | 4.5169        |
+| 0.0211 | 13200 | 4.533         |
+| 0.0213 | 13300 | 4.3725        |
+| 0.0214 | 13400 | 4.2911        |
+| 0.0216 | 13500 | 4.2261        |
+| 0.0218 | 13600 | 4.2467        |
+| 0.0219 | 13700 | 4.1558        |
+| 0.0221 | 13800 | 4.2794        |
+| 0.0222 | 13900 | 4.2383        |
+| 0.0224 | 14000 | 4.1654        |
+| 0.0226 | 14100 | 4.158         |
+| 0.0227 | 14200 | 4.1299        |
+| 0.0229 | 14300 | 4.1902        |
+| 0.0230 | 14400 | 3.7853        |
+| 0.0232 | 14500 | 4.0514        |
+| 0.0234 | 14600 | 4.1655        |
+| 0.0235 | 14700 | 4.051         |
+| 0.0237 | 14800 | 4.078         |
+| 0.0238 | 14900 | 4.1193        |
+| 0.0240 | 15000 | 4.1536        |
+| 0.0242 | 15100 | 3.935         |
+| 0.0243 | 15200 | 3.9535        |
+| 0.0245 | 15300 | 3.7051        |
+| 0.0246 | 15400 | 3.8329        |
+| 0.0248 | 15500 | 3.9412        |
+| 0.0250 | 15600 | 3.6668        |
+| 0.0251 | 15700 | 3.7758        |
+| 0.0253 | 15800 | 3.8805        |
+| 0.0254 | 15900 | 3.8848        |
+| 0.0256 | 16000 | 3.75          |
+| 0.0258 | 16100 | 3.5685        |
+| 0.0259 | 16200 | 3.7016        |
+| 0.0261 | 16300 | 4.0955        |
+| 0.0262 | 16400 | 3.7577        |
+| 0.0264 | 16500 | 3.7485        |
+| 0.0266 | 16600 | 3.8263        |
+| 0.0267 | 16700 | 3.6922        |
+| 0.0269 | 16800 | 3.6568        |
+| 0.0270 | 16900 | 3.7317        |
+| 0.0272 | 17000 | 3.5089        |
+| 0.0274 | 17100 | 3.7377        |
+| 0.0275 | 17200 | 3.6206        |
+| 0.0277 | 17300 | 3.3702        |
+| 0.0278 | 17400 | 3.5126        |
+| 0.0280 | 17500 | 3.4841        |
+| 0.0282 | 17600 | 3.1464        |
+| 0.0283 | 17700 | 3.7012        |
+| 0.0285 | 17800 | 3.5802        |
+| 0.0286 | 17900 | 3.4952        |
+| 0.0288 | 18000 | 3.1174        |
+| 0.0290 | 18100 | 3.3134        |
+| 0.0291 | 18200 | 3.3578        |
+| 0.0293 | 18300 | 3.0209        |
+| 0.0294 | 18400 | 3.3796        |
+| 0.0296 | 18500 | 3.2287        |
+| 0.0298 | 18600 | 3.1537        |
+| 0.0299 | 18700 | 2.9073        |
+| 0.0301 | 18800 | 3.3444        |
+| 0.0302 | 18900 | 3.1341        |
+| 0.0304 | 19000 | 2.8862        |
+| 0.0306 | 19100 | 3.2033        |
+| 0.0307 | 19200 | 3.2764        |
+| 0.0309 | 19300 | 3.0725        |
+| 0.0310 | 19400 | 3.0436        |
+| 0.0312 | 19500 | 3.3493        |
+| 0.0314 | 19600 | 3.0141        |
+| 0.0315 | 19700 | 2.779         |
+| 0.0317 | 19800 | 3.3543        |
+| 0.0318 | 19900 | 3.1526        |
+| 0.0320 | 20000 | 2.7896        |
+| 0.0322 | 20100 | 2.9398        |
+| 0.0323 | 20200 | 3.1254        |
+| 0.0325 | 20300 | 2.8832        |
+| 0.0326 | 20400 | 3.0542        |
+| 0.0328 | 20500 | 2.9722        |
+| 0.0330 | 20600 | 2.9321        |
+| 0.0331 | 20700 | 2.6448        |
+| 0.0333 | 20800 | 3.4006        |
+| 0.0334 | 20900 | 3.0022        |
+| 0.0336 | 21000 | 2.6366        |
+| 0.0338 | 21100 | 3.0112        |
+| 0.0339 | 21200 | 2.7856        |
+| 0.0341 | 21300 | 3.0967        |
+| 0.0342 | 21400 | 2.8754        |
+| 0.0344 | 21500 | 3.1269        |
+
+</details>
+
+### Framework Versions
+- Python: 3.8.10
+- Sentence Transformers: 3.1.1
+- Transformers: 4.45.2
+- PyTorch: 2.4.1+cu118
+- Accelerate: 1.0.1
+- Datasets: 3.0.1
+- Tokenizers: 0.20.3
+
+## 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",
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## 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.*
+-->

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