README.md

---
base_model: intfloat/multilingual-e5-small
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1204
- loss:TripletLoss
widget:
- source_sentence: How do I publish articles?
  sentences:
  - How do I publish an article?
  - Steps to meditate
  - How I publish my article on Yahoo?
- source_sentence: Who is the author of '1984'?
  sentences:
  - North America's largest lake by area
  - Writer of the novel '1984'
  - Who is the author of 'Pride and Prejudice'?
- source_sentence: What are adverbs? What are some kind of adverbs?
  sentences:
  - How can I get rid of flying cockroaches?
  - What are some examples of adverbs?
  - What's the difference between adverbial phrase and adverb phrase?
- source_sentence: Do you believe in astrology? Is it true?
  sentences:
  - Are horoscopes legitimate? Do they ever come true?
  - Today is my birthday. Why does no one wish me a happy birthday?
  - Do you believe in horoscope?
- source_sentence: After marriage, why do women have to change their surnames to their
    husband’s? Why can't they keep their maiden ones?
  sentences:
  - Steps to start a blog
  - After marriage, why do women have to change their surname?
  - Is it possible for an Indian woman not to change her surname after marriage?
model-index:
- name: SentenceTransformer based on intfloat/multilingual-e5-small
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: triplet validation
      type: triplet-validation
    metrics:
    - type: cosine_accuracy
      value: 0.9917355371900827
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.008264462809917356
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.9917355371900827
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9917355371900827
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9917355371900827
      name: Max Accuracy
---

# SentenceTransformer based on intfloat/multilingual-e5-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:** 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/e-small-triplet-balanced")
# Run inference
sentences = [
    "After marriage, why do women have to change their surnames to their husband’s? Why can't they keep their maiden ones?",
    'After marriage, why do women have to change their surname?',
    'Is it possible for an Indian woman not to change her surname after marriage?',
]
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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## Evaluation

### Metrics

#### Triplet
* Dataset: `triplet-validation`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy    | 0.9917     |
| dot_accuracy       | 0.0083     |
| manhattan_accuracy | 0.9917     |
| euclidean_accuracy | 0.9917     |
| **max_accuracy**   | **0.9917** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 1,204 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 12.25 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.44 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.68 tokens</li><li>max: 59 tokens</li></ul> |
* Samples:
  | anchor                                            | positive                                           | negative                                           |
  |:--------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|
  | <code>What are the ingredients of a pizza?</code> | <code>ingredients of pizza?</code>                 | <code>What are the ingredients of a burger?</code> |
  | <code>How does photosynthesis work?</code>        | <code>Explain the process of photosynthesis</code> | <code>How does respiration work?</code>            |
  | <code>How do I reset my password?</code>          | <code>Steps to reset password</code>               | <code>How do I change my username?</code>          |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 121 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                           |
  | details | <ul><li>min: 7 tokens</li><li>mean: 12.83 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.77 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.2 tokens</li><li>max: 48 tokens</li></ul> |
* Samples:
  | anchor                                                     | positive                                                 | negative                                                                                                     |
  |:-----------------------------------------------------------|:---------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|
  | <code>What is the best way to learn a new language?</code> | <code>How can I effectively learn a new language?</code> | <code>What is the fastest way to travel?</code>                                                              |
  | <code>Can people actively control their emotions?</code>   | <code>Does our mind control our emotions?</code>         | <code>How can I control my positive emotions for the people whom I love but they don't care about me?</code> |
  | <code>Which can be the best laptop under 30000?</code>     | <code>which laptop will be best under Rs 30,000?</code>  | <code>What is the best phone to buy under 30000 in India?</code>                                             |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 2
- `learning_rate`: 3e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 8
- `lr_scheduler_type`: reduce_lr_on_plateau
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused

#### 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`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 8
- `max_steps`: -1
- `lr_scheduler_type`: reduce_lr_on_plateau
- `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`: True
- `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_fused
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step    | Training Loss | loss       | triplet-validation_max_accuracy |
|:-------:|:-------:|:-------------:|:----------:|:-------------------------------:|
| 0.5263  | 10      | 4.8459        | -          | -                               |
| 1.0     | 19      | -             | 4.4155     | -                               |
| 1.0526  | 20      | 4.7205        | -          | -                               |
| 1.5789  | 30      | 4.5948        | -          | -                               |
| 2.0     | 38      | -             | 4.2163     | -                               |
| 2.1053  | 40      | 4.5125        | -          | -                               |
| 2.6316  | 50      | 4.4761        | -          | -                               |
| 3.0     | 57      | -             | 4.1338     | -                               |
| 3.1579  | 60      | 4.452         | -          | -                               |
| 3.6842  | 70      | 4.4082        | -          | -                               |
| 4.0     | 76      | -             | 4.0659     | -                               |
| 4.2105  | 80      | 4.3978        | -          | -                               |
| 4.7368  | 90      | 4.3495        | -          | -                               |
| 5.0     | 95      | -             | 4.0202     | -                               |
| 5.2632  | 100     | 4.287         | -          | -                               |
| 5.7895  | 110     | 4.2805        | -          | -                               |
| 6.0     | 114     | -             | 3.9441     | -                               |
| 6.3158  | 120     | 4.2631        | -          | -                               |
| 6.8421  | 130     | 4.213         | -          | -                               |
| 7.0     | 133     | -             | 3.8866     | -                               |
| 7.3684  | 140     | 4.1921        | -          | -                               |
| 7.8947  | 150     | 4.1854        | -          | -                               |
| **8.0** | **152** | **-**         | **3.8757** | **0.9917**                      |

* The bold row denotes the saved checkpoint.

### 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",
}
```

#### TripletLoss
```bibtex
@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification}, 
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

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