sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:548 loss:TripletLoss arxiv:1908.10084 arxiv:1703.07737 base_model:intfloat/multilingual-e5-small base_model:finetune:intfloat/multilingual-e5-small model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
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:548
- loss:TripletLoss
widget:
- source_sentence: What's the best way to learn a new language?
  sentences:
  - What is the fastest way to travel?
  - Current CEO of Apple Inc.
  - Best methods to learn a new language
- source_sentence: Where can I find the best sushi in town?
  sentences:
  - Paracetamol side effects
  - Where can I find the best pizza in town?
  - Where can I find the best sushi nearby?
- source_sentence: How to bake a chocolate cake?
  sentences:
  - How to make chocolate chip cookies?
  - Signs and symptoms of anxiety
  - Steps to bake a chocolate cake
- source_sentence: What is the largest lake in North America?
  sentences:
  - Steps to cook pasta
  - What is the largest river in North America?
  - North America's largest lake by area
- source_sentence: How many countries are in the European Union?
  sentences:
  - Formula to find the area of a circle
  - Number of countries in the European Union
  - How many continents are there?
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: 1.0
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.0
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 1.0
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 1.0
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 1.0
      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")
# Run inference
sentences = [
    'How many countries are in the European Union?',
    'Number of countries in the European Union',
    'How many continents are there?',
]
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    | 1.0     |
| dot_accuracy       | 0.0     |
| manhattan_accuracy | 1.0     |
| euclidean_accuracy | 1.0     |
| **max_accuracy**   | **1.0** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 548 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: 10.84 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.57 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.79 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
  | anchor                                                             | positive                                              | negative                                                               |
  |:-------------------------------------------------------------------|:------------------------------------------------------|:-----------------------------------------------------------------------|
  | <code>What is the difference between a laptop and a tablet?</code> | <code>Comparison between a laptop and a tablet</code> | <code>What is the difference between a laptop and a smartphone?</code> |
  | <code>How do I get to the nearest train station?</code>            | <code>Directions to the nearest train station</code>  | <code>How do I get to the airport?</code>                              |
  | <code>Who is the author of '1984'?</code>                          | <code>Writer of the novel '1984'</code>               | <code>Who is the author of 'Pride and Prejudice'?</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": 0.5
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 61 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: 10.36 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.28 tokens</li><li>max: 13 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.46 tokens</li><li>max: 14 tokens</li></ul> |
* Samples:
  | anchor                                                 | positive                                                 | negative                                                |
  |:-------------------------------------------------------|:---------------------------------------------------------|:--------------------------------------------------------|
  | <code>How many states are there in the USA?</code>     | <code>Total number of states in the United States</code> | <code>How many provinces are there in Canada?</code>    |
  | <code>What is the chemical formula for ethanol?</code> | <code>Molecular structure of ethanol</code>              | <code>What is the chemical formula for methanol?</code> |
  | <code>How to clean a laptop screen?</code>             | <code>Steps to safely clean a laptop display</code>      | <code>How to clean a laptop keyboard?</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": 0.5
  }
  ```

### 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`: 10
- `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`: 10
- `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 |
|:-------:|:------:|:-------------:|:----------:|:-------------------------------:|
| 1.0     | 9      | -             | 0.1078     | -                               |
| 1.1111  | 10     | 0.3352        | -          | -                               |
| 2.0     | 18     | -             | 0.0670     | -                               |
| 2.2222  | 20     | 0.1677        | -          | -                               |
| 3.0     | 27     | -             | 0.0434     | -                               |
| 3.3333  | 30     | 0.0832        | -          | -                               |
| 4.0     | 36     | -             | 0.0323     | -                               |
| 4.4444  | 40     | 0.063         | -          | -                               |
| 5.0     | 45     | -             | 0.0299     | -                               |
| 5.5556  | 50     | 0.0449        | -          | -                               |
| 6.0     | 54     | -             | 0.0273     | -                               |
| 6.6667  | 60     | 0.0357        | -          | -                               |
| 7.0     | 63     | -             | 0.0241     | -                               |
| 7.7778  | 70     | 0.0254        | -          | -                               |
| 8.0     | 72     | -             | 0.0224     | -                               |
| 8.8889  | 80     | 0.02          | -          | -                               |
| **9.0** | **81** | **-**         | **0.0211** | **-**                           |
| 10.0    | 90     | 0.0173        | 0.0216     | 1.0                             |

* 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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