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": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,679 @@
+---
+base_model: srikarvar/fine_tuned_model_5
+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
+- dot_accuracy@1
+- dot_accuracy@3
+- dot_accuracy@5
+- dot_accuracy@10
+- dot_precision@1
+- dot_precision@3
+- dot_precision@5
+- dot_precision@10
+- dot_recall@1
+- dot_recall@3
+- dot_recall@5
+- dot_recall@10
+- dot_ndcg@10
+- dot_mrr@10
+- dot_map@100
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:560
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: The `num_steps` parameter is employed to indicate the quantity
+    of steps when preparing the recipe.
+  sentences:
+  - The `num_steps` parameter is used to specify the number of steps when preparing
+    the recipe.
+  - The `rename_fields` function creates a new form with fields renamed to provided
+    names.
+  - The main difference between a ProductList and an InventoryList is that a ProductList
+    provides random access to the items, while an InventoryList updates progressively
+    as you browse the list.
+- source_sentence: The "extract" function creates a portion of the data without making
+    a copy, with the possibility to indicate an offset and size.
+  sentences:
+  - 'Sure! Here''s an example:'
+  - You can create a sauce by combining the ingredients and using the `with_stirring()`
+    function to mix them evenly.
+  - The "extract" function computes a zero-copy subset of the data, with the option
+    to specify an offset and length.
+- source_sentence: The `iterate_folder` function cycles through files inside a folder.
+  sentences:
+  - You can find it in the latest version of the user manual. Click on the provided
+    link to access the main version.
+  - The `iterate_folder` function iterates over files within a folder.
+  - It is a guide on how to process any type of module.
+- source_sentence: Technical descriptions of the framework’s APIs and modules can
+    be found in the reference section.
+  sentences:
+  - The `to_spreadsheet` method in the Plant class is used to convert the PlantData
+    to a `SpreadsheetRow` or `SpreadsheetTable`.
+  - Yes, there are technical details available in the reference section that explain
+    how the framework’s APIs and modules work.
+  - The `storage_dir` parameter is used to specify the directory to store ingredients.
+- source_sentence: Once you have completed your library script, you can generate a
+    library card and submit it to the server.
+  sentences:
+  - Once your library script is ready, you can create a library card and upload it
+    to the server.
+  - It replaces the document's header.
+  - Many product formats are supported, including CSV, XML, JSON, image, and video
+    files.
+model-index:
+- name: SentenceTransformer based on srikarvar/fine_tuned_model_5
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: e5 cogcache small refined
+      type: e5-cogcache-small-refined
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.9821428571428571
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9821428571428571
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 1.0
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.9821428571428571
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3273809523809524
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19999999999999998
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.9821428571428571
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9821428571428571
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 1.0
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9898335099655963
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9866071428571429
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9866071428571429
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.9821428571428571
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.9821428571428571
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 1.0
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 1.0
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.9821428571428571
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.3273809523809524
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.19999999999999998
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.09999999999999999
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.9821428571428571
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.9821428571428571
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 1.0
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 1.0
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.9898335099655963
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.9866071428571429
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.9866071428571429
+      name: Dot Map@100
+    - type: cosine_accuracy@1
+      value: 0.9821428571428571
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9821428571428571
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 1.0
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.9821428571428571
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3273809523809524
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19999999999999998
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.9821428571428571
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9821428571428571
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 1.0
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9898335099655963
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9866071428571429
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9866071428571429
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.9821428571428571
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.9821428571428571
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 1.0
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 1.0
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.9821428571428571
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.3273809523809524
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.19999999999999998
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.09999999999999999
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.9821428571428571
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.9821428571428571
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 1.0
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 1.0
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.9898335099655963
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.9866071428571429
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.9866071428571429
+      name: Dot Map@100
+---
+
+# SentenceTransformer based on srikarvar/fine_tuned_model_5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [srikarvar/fine_tuned_model_5](https://huggingface.co/srikarvar/fine_tuned_model_5) on the json 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:** [srikarvar/fine_tuned_model_5](https://huggingface.co/srikarvar/fine_tuned_model_5) <!-- at revision 4e4dc22ad09f760a0a35c55d14d2f89ebe2d2ff2 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 tokens
+- **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': 512, '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("srikarvar/fine_tuned_model_10")
+# Run inference
+sentences = [
+    'Once you have completed your library script, you can generate a library card and submit it to the server.',
+    'Once your library script is ready, you can create a library card and upload it to the server.',
+    "It replaces the document's header.",
+]
+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: `e5-cogcache-small-refined`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.9821     |
+| cosine_accuracy@3   | 0.9821     |
+| cosine_accuracy@5   | 1.0        |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.9821     |
+| cosine_precision@3  | 0.3274     |
+| cosine_precision@5  | 0.2        |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.9821     |
+| cosine_recall@3     | 0.9821     |
+| cosine_recall@5     | 1.0        |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.9898     |
+| cosine_mrr@10       | 0.9866     |
+| **cosine_map@100**  | **0.9866** |
+| dot_accuracy@1      | 0.9821     |
+| dot_accuracy@3      | 0.9821     |
+| dot_accuracy@5      | 1.0        |
+| dot_accuracy@10     | 1.0        |
+| dot_precision@1     | 0.9821     |
+| dot_precision@3     | 0.3274     |
+| dot_precision@5     | 0.2        |
+| dot_precision@10    | 0.1        |
+| dot_recall@1        | 0.9821     |
+| dot_recall@3        | 0.9821     |
+| dot_recall@5        | 1.0        |
+| dot_recall@10       | 1.0        |
+| dot_ndcg@10         | 0.9898     |
+| dot_mrr@10          | 0.9866     |
+| dot_map@100         | 0.9866     |
+
+#### Information Retrieval
+* Dataset: `e5-cogcache-small-refined`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.9821     |
+| cosine_accuracy@3   | 0.9821     |
+| cosine_accuracy@5   | 1.0        |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.9821     |
+| cosine_precision@3  | 0.3274     |
+| cosine_precision@5  | 0.2        |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.9821     |
+| cosine_recall@3     | 0.9821     |
+| cosine_recall@5     | 1.0        |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.9898     |
+| cosine_mrr@10       | 0.9866     |
+| **cosine_map@100**  | **0.9866** |
+| dot_accuracy@1      | 0.9821     |
+| dot_accuracy@3      | 0.9821     |
+| dot_accuracy@5      | 1.0        |
+| dot_accuracy@10     | 1.0        |
+| dot_precision@1     | 0.9821     |
+| dot_precision@3     | 0.3274     |
+| dot_precision@5     | 0.2        |
+| dot_precision@10    | 0.1        |
+| dot_recall@1        | 0.9821     |
+| dot_recall@3        | 0.9821     |
+| dot_recall@5        | 1.0        |
+| dot_recall@10       | 1.0        |
+| dot_ndcg@10         | 0.9898     |
+| dot_mrr@10          | 0.9866     |
+| dot_map@100         | 0.9866     |
+
+<!--
+## 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
+* Size: 560 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 560 samples:
+  |         | anchor                                                                            | positive                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 9 tokens</li><li>mean: 30.23 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 30.06 tokens</li><li>max: 98 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                     | positive                                                                                                                             |
+  |:---------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>It retrieves items from a list.</code>                                                                               | <code>It selects items from a list.</code>                                                                                           |
+  | <code>The goal of seasoning a cast iron pan is to create a non-stick surface and protect it from rust.</code>              | <code>The purpose of seasoning a cast iron pan is to create a non-stick surface and prevent rust.</code>                             |
+  | <code>The Spark manual covers topics like data analysis, machine learning, graph processing, and stream processing.</code> | <code>The Spark documentation covers topics such as data analysis, machine learning, graph processing, and stream processing.</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 1e-05
+- `warmup_ratio`: 0.1
+- `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`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 1e-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`: 3
+- `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`: False
+- `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
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | e5-cogcache-small-refined_cosine_map@100 |
+|:------:|:----:|:-------------:|:----------------------------------------:|
+| 0      | 0    | -             | 0.9777                                   |
+| 0.3125 | 10   | 0.0118        | -                                        |
+| 0.625  | 20   | 0.0025        | -                                        |
+| 0.9375 | 30   | 0.006         | -                                        |
+| 1.0    | 32   | -             | 0.9866                                   |
+| 1.25   | 40   | 0.0008        | -                                        |
+| 1.5625 | 50   | 0.0005        | -                                        |
+| 1.875  | 60   | 0.0011        | -                                        |
+| 2.0    | 64   | -             | 0.9866                                   |
+| 2.1875 | 70   | 0.0006        | -                                        |
+| 2.5    | 80   | 0.0003        | -                                        |
+| 2.8125 | 90   | 0.001         | -                                        |
+| 3.0    | 96   | -             | 0.9866                                   |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.1.0
+- Transformers: 4.41.2
+- PyTorch: 2.1.2+cu121
+- Accelerate: 0.34.2
+- 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",
+}
+```
+
+#### 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

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+{
+  "_name_or_path": "srikarvar/fine_tuned_model_5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.1.0",
+    "transformers": "4.41.2",
+    "pytorch": "2.1.2+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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modules.json

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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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special_tokens_map.json

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+    "rstrip": false,
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+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}