Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "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 +1,730 @@
+---
+base_model: BAAI/bge-small-en-v1.5
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:60323
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: No recipes found with these beef stock powder and orange juice!
+  sentences:
+  - Can you provide recipe ideas with beef stock powder and orange juice?
+  - What are some recipes that utilize jasmine rice and thai red curry paste effectively?
+  - What recipes incorporate broccoli and bacon into meals?
+- source_sentence: No recipes found with these nutmeg flower and angel hair rice noodles!
+  sentences:
+  - What dishes can be created with kale and bok choy?
+  - What recipes incorporate green zucchini and vegan ground beef into meals?
+  - Can you provide me with meal ideas using nutmeg flower and angel hair rice noodles?
+- source_sentence: No recipes found with these cinnamon and ground lamb!
+  sentences:
+  - Can you suggest dishes where cinnamon and ground lamb is key?
+  - What diet tags are relevant to Sneha's Aloo Baingan ?
+  - What recipes are there with toasted sesame oil and red lentils/masoor?
+- source_sentence: No recipes found with these red lentils/masoor and bok choy!
+  sentences:
+  - What are the culinary uses of chili sauce and sriracha?
+  - What are some ways to use canned tomato puree and frozen ube in recipes?
+  - What are some ideas for dishes with red lentils/masoor and bok choy?
+- source_sentence: No recipes found with these red onion and cubed stuffing!
+  sentences:
+  - Can you provide meal suggestions involving vanilla extract and brown lentil/black
+    masoor dal?
+  - What recipes incorporate methi (fenugreek) and honey in their ingredients?
+  - What culinary preparations can be made with red onion and cubed stuffing?
+model-index:
+- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 384
+      type: dim_384
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.9819483813217962
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9976130091004028
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9995524392063255
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.9819483813217962
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.33253766970013426
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1999104878412651
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.9819483813217962
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9976130091004028
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9995524392063255
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9923670621371893
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9897597379993318
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9897597379993323
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.9812024466656721
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.997463822169178
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9998508130687752
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.9812024466656721
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3324879407230593
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19997016261375503
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.9812024466656721
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.997463822169178
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9998508130687752
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9921395779775503
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9894450246158434
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9894450246158436
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.979561390422199
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9970162613755035
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9998508130687752
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.979561390422199
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3323387537918345
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19997016261375505
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.979561390422199
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9970162613755035
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9998508130687752
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9913010184783637
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9883310955293644
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9883310955293649
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.9816500074593466
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9968670744442787
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9997016261375503
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.9816500074593466
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3322890248147595
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19994032522751004
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.9816500074593466
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9968670744442787
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9997016261375503
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9920343842432707
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9893333120209138
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9893333120209146
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on BAAI/bge-small-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 tokens
+- **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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, '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("Adi-0-0-Gupta/Embedding")
+# Run inference
+sentences = [
+    'No recipes found with these red onion and cubed stuffing!',
+    'What culinary preparations can be made with red onion and cubed stuffing?',
+    'Can you provide meal suggestions involving vanilla extract and brown lentil/black masoor dal?',
+]
+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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+* Dataset: `dim_384`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.9819     |
+| cosine_accuracy@3   | 0.9976     |
+| cosine_accuracy@5   | 0.9996     |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.9819     |
+| cosine_precision@3  | 0.3325     |
+| cosine_precision@5  | 0.1999     |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.9819     |
+| cosine_recall@3     | 0.9976     |
+| cosine_recall@5     | 0.9996     |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.9924     |
+| cosine_mrr@10       | 0.9898     |
+| **cosine_map@100**  | **0.9898** |
+
+#### Information Retrieval
+* Dataset: `dim_256`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.9812     |
+| cosine_accuracy@3   | 0.9975     |
+| cosine_accuracy@5   | 0.9999     |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.9812     |
+| cosine_precision@3  | 0.3325     |
+| cosine_precision@5  | 0.2        |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.9812     |
+| cosine_recall@3     | 0.9975     |
+| cosine_recall@5     | 0.9999     |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.9921     |
+| cosine_mrr@10       | 0.9894     |
+| **cosine_map@100**  | **0.9894** |
+
+#### Information Retrieval
+* Dataset: `dim_128`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.9796     |
+| cosine_accuracy@3   | 0.997      |
+| cosine_accuracy@5   | 0.9999     |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.9796     |
+| cosine_precision@3  | 0.3323     |
+| cosine_precision@5  | 0.2        |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.9796     |
+| cosine_recall@3     | 0.997      |
+| cosine_recall@5     | 0.9999     |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.9913     |
+| cosine_mrr@10       | 0.9883     |
+| **cosine_map@100**  | **0.9883** |
+
+#### Information Retrieval
+* Dataset: `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.9817     |
+| cosine_accuracy@3   | 0.9969     |
+| cosine_accuracy@5   | 0.9997     |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.9817     |
+| cosine_precision@3  | 0.3323     |
+| cosine_precision@5  | 0.1999     |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.9817     |
+| cosine_recall@3     | 0.9969     |
+| cosine_recall@5     | 0.9997     |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.992      |
+| cosine_mrr@10       | 0.9893     |
+| **cosine_map@100**  | **0.9893** |
+
+<!--
+## 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: 60,323 training samples
+* Columns: <code>positive</code> and <code>anchor</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | positive                                                                            | anchor                                                                            |
+  |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                            |
+  | details | <ul><li>min: 11 tokens</li><li>mean: 21.41 tokens</li><li>max: 503 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 16.8 tokens</li><li>max: 31 tokens</li></ul> |
+* Samples:
+  | positive                                                                                  | anchor                                                                                            |
+  |:------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|
+  | <code>No recipes found with these indian cottage cheese (paneer) and bitter melon!</code> | <code>What are some culinary options with indian cottage cheese (paneer) and bitter melon?</code> |
+  | <code>No recipes found with these curry leaf and rice cakes!</code>                       | <code>What recipes can be made using curry leaf and rice cakes?</code>                            |
+  | <code>No recipes found with these bacon and  rosemary!</code>                             | <code>What are the different culinary recipes that use bacon and  rosemary?</code>                |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          384,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `gradient_accumulation_steps`: 8
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 10
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `tf32`: True
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `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`: 8
+- `eval_accumulation_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`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `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`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: True
+- `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_fused
+- `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
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 |
+|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 0.0848 | 10   | 3.9258        | -                      | -                      | -                      | -                     |
+| 0.1697 | 20   | 3.0513        | -                      | -                      | -                      | -                     |
+| 0.2545 | 30   | 1.6368        | -                      | -                      | -                      | -                     |
+| 0.3393 | 40   | 0.5491        | -                      | -                      | -                      | -                     |
+| 0.4242 | 50   | 0.1541        | -                      | -                      | -                      | -                     |
+| 0.5090 | 60   | 0.0615        | -                      | -                      | -                      | -                     |
+| 0.5938 | 70   | 0.0426        | -                      | -                      | -                      | -                     |
+| 0.6787 | 80   | 0.037         | -                      | -                      | -                      | -                     |
+| 0.7635 | 90   | 0.0312        | -                      | -                      | -                      | -                     |
+| 0.8484 | 100  | 0.0246        | -                      | -                      | -                      | -                     |
+| 0.9332 | 110  | 0.029         | -                      | -                      | -                      | -                     |
+| 0.9926 | 117  | -             | 0.9855                 | 0.9869                 | 0.9869                 | 0.9855                |
+| 1.0180 | 120  | 0.0205        | -                      | -                      | -                      | -                     |
+| 1.1029 | 130  | 0.0212        | -                      | -                      | -                      | -                     |
+| 1.1877 | 140  | 0.0196        | -                      | -                      | -                      | -                     |
+| 1.2725 | 150  | 0.0157        | -                      | -                      | -                      | -                     |
+| 1.3574 | 160  | 0.0174        | -                      | -                      | -                      | -                     |
+| 1.4422 | 170  | 0.0152        | -                      | -                      | -                      | -                     |
+| 1.5270 | 180  | 0.0155        | -                      | -                      | -                      | -                     |
+| 1.6119 | 190  | 0.0133        | -                      | -                      | -                      | -                     |
+| 1.6967 | 200  | 0.0173        | -                      | -                      | -                      | -                     |
+| 1.7815 | 210  | 0.014         | -                      | -                      | -                      | -                     |
+| 1.8664 | 220  | 0.0127        | -                      | -                      | -                      | -                     |
+| 1.9512 | 230  | 0.0116        | -                      | -                      | -                      | -                     |
+| 1.9936 | 235  | -             | 0.9883                 | 0.9894                 | 0.9898                 | 0.9893                |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.1.2+cu121
+- Accelerate: 0.31.0
+- Datasets: 2.19.1
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning}, 
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### 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 +1,31 @@
+{
+  "_name_or_path": "./Model/",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.3.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce9ddad92db5d1db4669ffbfcc7ab8a5a7993f675bd55ad33bb2030c2a1ceeb9
+size 133462128

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": 512,
+  "do_lower_case": true
+}

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 +1,64 @@
+{
+  "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,
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+    "102": {
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "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]"
+}