README.md

---
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 main objective of the System Logs documentation is to demonstrate
    how to utilize the 📋 Logs system to access and manipulate logs of any format or
    type.
  sentences:
  - The purpose of the System Logs documentation is to provide information on how
    to use the 📋 Logs system to store and work with logs of any format or type.
  - 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.
  - The most recommended way to clean kitchen surfaces is with a microfiber cloth.
- source_sentence: The main repository page can be accessed by clicking on the link.
  sentences:
  - The `to_absolute` function translates a `TaskInstruction` instance into a list
    of absolute instructions, which are then combined together.
  - No, ACTIVATE_X doesn't exist in version 3.0.
  - It exists in the main repository. You can click on the provided link to redirect
    to the main repository page.
- source_sentence: The documentation does not specify what type of value is returned
    by the `fetch_data` function.
  sentences:
  - The purpose of this document is to provide documentation for the Plugin library.
  - The return type of the `fetch_data` function is not specified in the current API
    documentation.
  - 'The `from_dictionary` function takes the following parameters:

    - `data` (Union[dict, Mapping]): A mapping of keys to values or Python objects.

    - `schema` (Schema, optional): If not passed, will be inferred from the Mapping
    values.

    - `metadata` (Union[dict, Mapping], optional): Optional metadata for the schema
    (if inferred).'
- source_sentence: The aim of the Gardening.Fertilization class is to carry out the
    application of fertilizers in the garden.
  sentences:
  - The `iterate_folder` function iterates over files within a folder.
  - The purpose of the Gardening.Fertilization class is to apply fertilizers in the
    garden.
  - It may be more convenient for the reader to not specify a section when browsing
    a collection because a suitable default may be an aggregated section that displays
    all genres if the reader doesn’t request a particular one.
- source_sentence: Two kinds of cooking methods exist, baking and frying.
  sentences:
  - There are two types of cooking methods, baking and frying.
  - The purpose of the given recipe is to provide instructions for making lasagna.
  - To get the full path to the locally extracted file, we need to join the path of
    the directory where the archive is extracted to and the relative image file path.
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: 0.9642857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 1.0
      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: 0.9642857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3333333333333333
      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: 0.9642857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 1.0
      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: 0.9844808884566332
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9791666666666666
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9791666666666667
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.9642857142857143
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 1.0
      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: 0.9642857142857143
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.3333333333333333
      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: 0.9642857142857143
      name: Dot Recall@1
    - type: dot_recall@3
      value: 1.0
      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: 0.9844808884566332
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.9791666666666666
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.9791666666666667
      name: Dot Map@100
    - type: cosine_accuracy@1
      value: 0.9642857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 1.0
      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: 0.9642857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3333333333333333
      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: 0.9642857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 1.0
      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: 0.9844808884566332
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9791666666666666
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9791666666666667
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.9642857142857143
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 1.0
      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: 0.9642857142857143
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.3333333333333333
      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: 0.9642857142857143
      name: Dot Recall@1
    - type: dot_recall@3
      value: 1.0
      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: 0.9844808884566332
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.9791666666666666
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.9791666666666667
      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_16")
# Run inference
sentences = [
    'Two kinds of cooking methods exist, baking and frying.',
    'There are two types of cooking methods, baking and frying.',
    'The purpose of the given recipe is to provide instructions for making lasagna.',
]
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]
```

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### Direct Usage (Transformers)

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</details>
-->

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
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### Out-of-Scope Use

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## 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   | 0.9643     |
| cosine_accuracy@3   | 1.0        |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.9643     |
| cosine_precision@3  | 0.3333     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.9643     |
| cosine_recall@3     | 1.0        |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9845     |
| cosine_mrr@10       | 0.9792     |
| **cosine_map@100**  | **0.9792** |
| dot_accuracy@1      | 0.9643     |
| dot_accuracy@3      | 1.0        |
| dot_accuracy@5      | 1.0        |
| dot_accuracy@10     | 1.0        |
| dot_precision@1     | 0.9643     |
| dot_precision@3     | 0.3333     |
| dot_precision@5     | 0.2        |
| dot_precision@10    | 0.1        |
| dot_recall@1        | 0.9643     |
| dot_recall@3        | 1.0        |
| dot_recall@5        | 1.0        |
| dot_recall@10       | 1.0        |
| dot_ndcg@10         | 0.9845     |
| dot_mrr@10          | 0.9792     |
| dot_map@100         | 0.9792     |

#### 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   | 0.9643     |
| cosine_accuracy@3   | 1.0        |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.9643     |
| cosine_precision@3  | 0.3333     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.9643     |
| cosine_recall@3     | 1.0        |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9845     |
| cosine_mrr@10       | 0.9792     |
| **cosine_map@100**  | **0.9792** |
| dot_accuracy@1      | 0.9643     |
| dot_accuracy@3      | 1.0        |
| dot_accuracy@5      | 1.0        |
| dot_accuracy@10     | 1.0        |
| dot_precision@1     | 0.9643     |
| dot_precision@3     | 0.3333     |
| dot_precision@5     | 0.2        |
| dot_precision@10    | 0.1        |
| dot_recall@1        | 0.9643     |
| dot_recall@3        | 1.0        |
| dot_recall@5        | 1.0        |
| dot_recall@10       | 1.0        |
| dot_ndcg@10         | 0.9845     |
| dot_mrr@10          | 0.9792     |
| dot_map@100         | 0.9792     |

<!--
## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## 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: 30.72 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 30.52 tokens</li><li>max: 98 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                             | positive                                                                                                                   |
  |:-----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------|
  | <code>The function assists in the preprocessing of the whole module in one go.</code>                                              | <code>The function helps preprocess your entire module at once.</code>                                                     |
  | <code>The `num_threads` parameter determines the quantity of threads used when downloading and processing the data locally.</code> | <code>The `num_threads` parameter specifies the number of threads when downloading and processing the data locally.</code> |
  | <code>The `map()` function can be used to apply transformations to all elements of a model.</code>                                 | <code>The `map()` function can apply transforms over an entire model.</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
- `num_train_epochs`: 5
- `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`: 5e-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`: 5
- `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    | -             | 0.9702                                   |
| 0.3125 | 10   | 0.0171        | -                                        |
| 0.625  | 20   | 0.0042        | -                                        |
| 0.9375 | 30   | 0.0011        | -                                        |
| 1.0    | 32   | -             | 0.9792                                   |
| 1.25   | 40   | 0.0062        | -                                        |
| 1.5625 | 50   | 0.0001        | -                                        |
| 1.875  | 60   | 0.0002        | -                                        |
| 2.0    | 64   | -             | 0.9792                                   |
| 2.1875 | 70   | 0.0001        | -                                        |
| 2.5    | 80   | 0.0005        | -                                        |
| 2.8125 | 90   | 0.0001        | -                                        |
| 3.0    | 96   | -             | 0.9792                                   |
| 3.125  | 100  | 0.0001        | -                                        |
| 3.4375 | 110  | 0.0002        | -                                        |
| 3.75   | 120  | 0.0001        | -                                        |
| 4.0    | 128  | -             | 0.9792                                   |
| 4.0625 | 130  | 0.0001        | -                                        |
| 4.375  | 140  | 0.0           | -                                        |
| 4.6875 | 150  | 0.0001        | -                                        |
| 5.0    | 160  | 0.0001        | 0.9792                                   |


### 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}
}
```

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