---
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.*
-->