README.md

---
base_model: srikarvar/e5-cogcache-small
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
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:246
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: What is the time now?
  sentences:
  - Signs of COVID-19 infection
  - Signs indicating anxiety disorder
  - What's the time?
- source_sentence: What is the largest desert in the world?
  sentences:
  - Painter of the Mona Lisa
  - Name of the biggest desert
  - Name the capital of Germany
- source_sentence: How to open a bank account in the UK?
  sentences:
  - Guide to opening a bank account in the UK
  - Who's the writer of "To Kill a Mockingbird"?
  - What are the ingredients of a pizza
- source_sentence: Can you help me with my homework?
  sentences:
  - I need help with my homework
  - Effective ways to learn a new language
  - Can you explain the process of photosynthesis?
- source_sentence: What is the best way to save money?
  sentences:
  - Methods for saving money efficiently
  - Which city is the capital of France?
  - Bitcoin price update
model-index:
- name: e5 cogcache small refined
  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: 0.35714285714285715
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8928571428571429
      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: 0.35714285714285715
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.29761904761904756
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.20000000000000004
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10000000000000002
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.35714285714285715
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8928571428571429
      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: 0.6976351587432169
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5964285714285715
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5964285714285714
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.35714285714285715
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.8928571428571429
      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: 0.35714285714285715
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.29761904761904756
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.20000000000000004
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.10000000000000002
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.35714285714285715
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.8928571428571429
      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: 0.6976351587432169
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.5964285714285715
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.5964285714285714
      name: Dot Map@100
    - type: cosine_accuracy@1
      value: 0.39285714285714285
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8571428571428571
      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: 0.39285714285714285
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.28571428571428564
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.20000000000000004
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10000000000000002
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.39285714285714285
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8571428571428571
      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: 0.7176925270162473
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6232142857142857
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6232142857142857
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.39285714285714285
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.8571428571428571
      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: 0.39285714285714285
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.28571428571428564
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.20000000000000004
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.10000000000000002
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.39285714285714285
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.8571428571428571
      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: 0.7176925270162473
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.6232142857142857
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.6232142857142857
      name: Dot Map@100
---

# e5 cogcache small refined

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [srikarvar/e5-cogcache-small](https://huggingface.co/srikarvar/e5-cogcache-small). 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/e5-cogcache-small](https://huggingface.co/srikarvar/e5-cogcache-small) <!-- at revision f3c1616bf0f4f22736df018fddf4c5e039d5f32a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 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/e5-cogcache-small-refined")
# Run inference
sentences = [
    'What is the best way to save money?',
    'Methods for saving money efficiently',
    'Which city is the capital of France?',
]
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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You can finetune this model on your own dataset.

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

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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   | 0.3571     |
| cosine_accuracy@3   | 0.8929     |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.3571     |
| cosine_precision@3  | 0.2976     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.3571     |
| cosine_recall@3     | 0.8929     |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.6976     |
| cosine_mrr@10       | 0.5964     |
| **cosine_map@100**  | **0.5964** |
| dot_accuracy@1      | 0.3571     |
| dot_accuracy@3      | 0.8929     |
| dot_accuracy@5      | 1.0        |
| dot_accuracy@10     | 1.0        |
| dot_precision@1     | 0.3571     |
| dot_precision@3     | 0.2976     |
| dot_precision@5     | 0.2        |
| dot_precision@10    | 0.1        |
| dot_recall@1        | 0.3571     |
| dot_recall@3        | 0.8929     |
| dot_recall@5        | 1.0        |
| dot_recall@10       | 1.0        |
| dot_ndcg@10         | 0.6976     |
| dot_mrr@10          | 0.5964     |
| dot_map@100         | 0.5964     |

#### 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   | 0.3929     |
| cosine_accuracy@3   | 0.8571     |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.3929     |
| cosine_precision@3  | 0.2857     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.3929     |
| cosine_recall@3     | 0.8571     |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.7177     |
| cosine_mrr@10       | 0.6232     |
| **cosine_map@100**  | **0.6232** |
| dot_accuracy@1      | 0.3929     |
| dot_accuracy@3      | 0.8571     |
| dot_accuracy@5      | 1.0        |
| dot_accuracy@10     | 1.0        |
| dot_precision@1     | 0.3929     |
| dot_precision@3     | 0.2857     |
| dot_precision@5     | 0.2        |
| dot_precision@10    | 0.1        |
| dot_recall@1        | 0.3929     |
| dot_recall@3        | 0.8571     |
| dot_recall@5        | 1.0        |
| dot_recall@10       | 1.0        |
| dot_ndcg@10         | 0.7177     |
| dot_mrr@10          | 0.6232     |
| dot_map@100         | 0.6232     |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 246 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                        |
  |:--------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                          |
  | details | <ul><li>min: 6 tokens</li><li>mean: 9.59 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.3 tokens</li><li>max: 17 tokens</li></ul> |
* Samples:
  | anchor                                        | positive                                          |
  |:----------------------------------------------|:--------------------------------------------------|
  | <code>How to open a bank account?</code>      | <code>Procedure for opening a bank account</code> |
  | <code>Who wrote 'Pride and Prejudice'?</code> | <code>Author of 'Pride and Prejudice'</code>      |
  | <code>What is the capital of Canada?</code>   | <code>Canada's capital city</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`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `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`: steps
- `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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: 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 | e5-cogcache-small-refined_cosine_map@100 |
|:-----:|:----:|:----------------------------------------:|
| 0     | 0    | 0.5964                                   |
| 1.0   | 16   | 0.6232                                   |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.32.1
- 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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