---
base_model: intfloat/multilingual-e5-small
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2000
- loss:OnlineContrastiveLoss
widget:
- source_sentence: How do I sign up for a new account?
  sentences:
  - How do I book a flight online?
  - Can I withdraw money from my bank?
  - What is the process for creating a new account?
- source_sentence: How can I enhance my English skills?
  sentences:
  - What are the ingredients of a pizza?
  - How can I improve my English?
  - What are the ingredients of a pizza?
- source_sentence: Where can I buy a new bicycle?
  sentences:
  - What is the importance of a balanced diet?
  - How do I update my address?
  - Where can I buy a new laptop?
- source_sentence: What steps do I need to follow to log into the company's internal
    network?
  sentences:
  - Who wrote the book "To Kill a Mockingbird"?
  - How do I reset my password?
  - How do I access the company's intranet?
- source_sentence: How can I improve my Spanish?
  sentences:
  - How can I lose weight?
  - How can I improve my English?
  - What is the most effective way to lose weight?
model-index:
- name: e5 cogcache small
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: base
      type: base
    metrics:
    - type: cosine_accuracy
      value: 0.8923076923076924
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.8427294492721558
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9166666666666666
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.8427294492721558
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9166666666666666
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9166666666666666
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9540451716910969
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.8923076923076924
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 0.842729389667511
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.9166666666666666
      name: Dot F1
    - type: dot_f1_threshold
      value: 0.842729389667511
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.9166666666666666
      name: Dot Precision
    - type: dot_recall
      value: 0.9166666666666666
      name: Dot Recall
    - type: dot_ap
      value: 0.9540451716910969
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.8846153846153846
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 10.00046157836914
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.9142857142857143
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 10.00046157836914
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.8791208791208791
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.9523809523809523
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.9533842444122883
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.8923076923076924
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 0.5608394742012024
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.9166666666666666
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 0.5608394742012024
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.9166666666666666
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9166666666666666
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.9540451716910969
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.8923076923076924
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 10.00046157836914
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.9166666666666666
      name: Max F1
    - type: max_f1_threshold
      value: 10.00046157836914
      name: Max F1 Threshold
    - type: max_precision
      value: 0.9166666666666666
      name: Max Precision
    - type: max_recall
      value: 0.9523809523809523
      name: Max Recall
    - type: max_ap
      value: 0.9540451716910969
      name: Max Ap
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: tuned
      type: tuned
    metrics:
    - type: cosine_accuracy
      value: 0.8923076923076924
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.8427294492721558
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9166666666666666
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.8427294492721558
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9166666666666666
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9166666666666666
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9540451716910969
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.8923076923076924
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 0.842729389667511
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.9166666666666666
      name: Dot F1
    - type: dot_f1_threshold
      value: 0.842729389667511
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.9166666666666666
      name: Dot Precision
    - type: dot_recall
      value: 0.9166666666666666
      name: Dot Recall
    - type: dot_ap
      value: 0.9540451716910969
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.8846153846153846
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 10.00046157836914
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.9142857142857143
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 10.00046157836914
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.8791208791208791
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.9523809523809523
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.9533842444122883
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.8923076923076924
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 0.5608394742012024
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.9166666666666666
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 0.5608394742012024
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.9166666666666666
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9166666666666666
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.9540451716910969
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.8923076923076924
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 10.00046157836914
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.9166666666666666
      name: Max F1
    - type: max_f1_threshold
      value: 10.00046157836914
      name: Max F1 Threshold
    - type: max_precision
      value: 0.9166666666666666
      name: Max Precision
    - type: max_recall
      value: 0.9523809523809523
      name: Max Recall
    - type: max_ap
      value: 0.9540451716910969
      name: Max Ap
---

# e5 cogcache small

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision fd1525a9fd15316a2d503bf26ab031a61d056e98 -->
- **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-small-cogcachedata")
# Run inference
sentences = [
    'How can I improve my Spanish?',
    'How can I improve my English?',
    'How can I lose weight?',
]
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)

<details><summary>Click to see the direct usage in Transformers</summary>

</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

#### Binary Classification
* Dataset: `base`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value     |
|:-----------------------------|:----------|
| cosine_accuracy              | 0.8923    |
| cosine_accuracy_threshold    | 0.8427    |
| cosine_f1                    | 0.9167    |
| cosine_f1_threshold          | 0.8427    |
| cosine_precision             | 0.9167    |
| cosine_recall                | 0.9167    |
| cosine_ap                    | 0.954     |
| dot_accuracy                 | 0.8923    |
| dot_accuracy_threshold       | 0.8427    |
| dot_f1                       | 0.9167    |
| dot_f1_threshold             | 0.8427    |
| dot_precision                | 0.9167    |
| dot_recall                   | 0.9167    |
| dot_ap                       | 0.954     |
| manhattan_accuracy           | 0.8846    |
| manhattan_accuracy_threshold | 10.0005   |
| manhattan_f1                 | 0.9143    |
| manhattan_f1_threshold       | 10.0005   |
| manhattan_precision          | 0.8791    |
| manhattan_recall             | 0.9524    |
| manhattan_ap                 | 0.9534    |
| euclidean_accuracy           | 0.8923    |
| euclidean_accuracy_threshold | 0.5608    |
| euclidean_f1                 | 0.9167    |
| euclidean_f1_threshold       | 0.5608    |
| euclidean_precision          | 0.9167    |
| euclidean_recall             | 0.9167    |
| euclidean_ap                 | 0.954     |
| max_accuracy                 | 0.8923    |
| max_accuracy_threshold       | 10.0005   |
| max_f1                       | 0.9167    |
| max_f1_threshold             | 10.0005   |
| max_precision                | 0.9167    |
| max_recall                   | 0.9524    |
| **max_ap**                   | **0.954** |

#### Binary Classification
* Dataset: `tuned`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value     |
|:-----------------------------|:----------|
| cosine_accuracy              | 0.8923    |
| cosine_accuracy_threshold    | 0.8427    |
| cosine_f1                    | 0.9167    |
| cosine_f1_threshold          | 0.8427    |
| cosine_precision             | 0.9167    |
| cosine_recall                | 0.9167    |
| cosine_ap                    | 0.954     |
| dot_accuracy                 | 0.8923    |
| dot_accuracy_threshold       | 0.8427    |
| dot_f1                       | 0.9167    |
| dot_f1_threshold             | 0.8427    |
| dot_precision                | 0.9167    |
| dot_recall                   | 0.9167    |
| dot_ap                       | 0.954     |
| manhattan_accuracy           | 0.8846    |
| manhattan_accuracy_threshold | 10.0005   |
| manhattan_f1                 | 0.9143    |
| manhattan_f1_threshold       | 10.0005   |
| manhattan_precision          | 0.8791    |
| manhattan_recall             | 0.9524    |
| manhattan_ap                 | 0.9534    |
| euclidean_accuracy           | 0.8923    |
| euclidean_accuracy_threshold | 0.5608    |
| euclidean_f1                 | 0.9167    |
| euclidean_f1_threshold       | 0.5608    |
| euclidean_precision          | 0.9167    |
| euclidean_recall             | 0.9167    |
| euclidean_ap                 | 0.954     |
| max_accuracy                 | 0.8923    |
| max_accuracy_threshold       | 10.0005   |
| max_f1                       | 0.9167    |
| max_f1_threshold             | 10.0005   |
| max_precision                | 0.9167    |
| max_recall                   | 0.9524    |
| **max_ap**                   | **0.954** |

<!--
## 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

#### Unnamed Dataset


* Size: 2,000 training samples
* Columns: <code>sentence2</code>, <code>sentence1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence2                                                                         | sentence1                                                                         | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 13.29 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.24 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>0: ~55.10%</li><li>1: ~44.90%</li></ul> |
* Samples:
  | sentence2                                        | sentence1                                         | label          |
  |:-------------------------------------------------|:--------------------------------------------------|:---------------|
  | <code>What are the ingredients of a pizza</code> | <code>What are the ingredients of a pizza?</code> | <code>1</code> |
  | <code>What are the ingredients of pizza</code>   | <code>What are the ingredients of a pizza?</code> | <code>1</code> |
  | <code>What are ingredients of pizza</code>       | <code>What are the ingredients of a pizza?</code> | <code>1</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

### Evaluation Dataset

#### Unnamed Dataset


* Size: 130 evaluation samples
* Columns: <code>sentence2</code>, <code>sentence1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence2                                                                         | sentence1                                                                         | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                             |
  | details | <ul><li>min: 5 tokens</li><li>mean: 11.48 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.85 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>0: ~35.38%</li><li>1: ~64.62%</li></ul> |
* Samples:
  | sentence2                                        | sentence1                                         | label          |
  |:-------------------------------------------------|:--------------------------------------------------|:---------------|
  | <code>What are the ingredients of a pizza</code> | <code>What are the ingredients of a pizza?</code> | <code>1</code> |
  | <code>What are the ingredients of pizza</code>   | <code>What are the ingredients of a pizza?</code> | <code>1</code> |
  | <code>What are ingredients of pizza</code>       | <code>What are the ingredients of a pizza?</code> | <code>1</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 6
- `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`: 6
- `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 | loss   | base_max_ap | tuned_max_ap |
|:-----:|:----:|:-------------:|:------:|:-----------:|:------------:|
| 0     | 0    | -             | -      | 0.7430      | -            |
| 1.0   | 125  | -             | 0.5464 | 0.7914      | -            |
| 2.0   | 250  | -             | 0.2451 | 0.9018      | -            |
| 3.0   | 375  | -             | 0.1717 | 0.9460      | -            |
| 4.0   | 500  | 0.24          | 0.1490 | 0.9532      | -            |
| 5.0   | 625  | -             | 0.1598 | 0.9523      | -            |
| 6.0   | 750  | -             | 0.1382 | 0.9540      | 0.9540       |


### 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",
}
```

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