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---
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:546
- loss:TripletLoss
widget:
- source_sentence: How to cook a turkey?
sentences:
- How to make a turkey sandwich?
- World's biggest desert by area
- Steps to roast a turkey
- source_sentence: What is the best way to learn a new language?
sentences:
- Author of the play 'Hamlet'
- What is the fastest way to travel?
- How can I effectively learn a new language?
- source_sentence: Who wrote 'To Kill a Mockingbird'?
sentences:
- Who wrote 'The Great Gatsby'?
- How can I effectively save money?
- Author of 'To Kill a Mockingbird'
- source_sentence: Who was the first person to climb Mount Everest?
sentences:
- Steps to visit the Great Wall of China
- Who was the first person to climb K2?
- First climber to reach the summit of Everest
- source_sentence: What is the capital city of Canada?
sentences:
- First circumnavigator of the globe
- What is the capital of Canada?
- What is the capital city of Australia?
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: 0.9836065573770492
name: Cosine Accuracy
- type: dot_accuracy
value: 0.01639344262295082
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.9836065573770492
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.9836065573770492
name: Euclidean Accuracy
- type: max_accuracy
value: 0.9836065573770492
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/multilingual-e5-small-triplet-final")
# Run inference
sentences = [
'What is the capital city of Canada?',
'What is the capital of Canada?',
'What is the capital city of Australia?',
]
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 | 0.9836 |
| dot_accuracy | 0.0164 |
| manhattan_accuracy | 0.9836 |
| euclidean_accuracy | 0.9836 |
| **max_accuracy** | **0.9836** |
<!--
## 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.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 546 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: 10.78 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.52 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.75 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------|:----------------------------------------------|:-------------------------------------------------------|
| <code>What is the capital of Brazil?</code> | <code>Capital city of Brazil</code> | <code>What is the capital of Argentina?</code> |
| <code>How do I install Python on my computer?</code> | <code>How do I set up Python on my PC?</code> | <code>How do I uninstall Python on my computer?</code> |
| <code>How do I apply for a credit card?</code> | <code>How do I get a credit card?</code> | <code>How do I cancel a credit card?</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: 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: 10.66 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.43 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.54 tokens</li><li>max: 17 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------|:---------------------------------------------------------|:-----------------------------------------------------|
| <code>How to create a podcast?</code> | <code>Steps to start a podcast</code> | <code>How to create a vlog?</code> |
| <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 population of India?</code> | <code>How many people live in India?</code> | <code>What is the population of China?</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`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 2
- `learning_rate`: 5e-06
- `weight_decay`: 0.01
- `num_train_epochs`: 12
- `lr_scheduler_type`: cosine
- `warmup_steps`: 50
- `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`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-06
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 12
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 50
- `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 |
|:-----------:|:-------:|:-------------:|:----------:|:-------------------------------:|
| 0.5714 | 10 | 4.9735 | - | - |
| 0.9714 | 17 | - | 4.9198 | - |
| 1.1429 | 20 | 4.9596 | - | - |
| 1.7143 | 30 | 4.9357 | - | - |
| 2.0 | 35 | - | 4.8494 | - |
| 2.2857 | 40 | 4.896 | - | - |
| 2.8571 | 50 | 4.8587 | - | - |
| 2.9714 | 52 | - | 4.7479 | - |
| 3.4286 | 60 | 4.8265 | - | - |
| 4.0 | 70 | 4.7706 | 4.6374 | - |
| 4.5714 | 80 | 4.7284 | - | - |
| 4.9714 | 87 | - | 4.5422 | - |
| 5.1429 | 90 | 4.6767 | - | - |
| 5.7143 | 100 | 4.653 | - | - |
| 6.0 | 105 | - | 4.4474 | - |
| 6.2857 | 110 | 4.6234 | - | - |
| 6.8571 | 120 | 4.5741 | - | - |
| 6.9714 | 122 | - | 4.3708 | - |
| 7.4286 | 130 | 4.5475 | - | - |
| 8.0 | 140 | 4.5206 | 4.3162 | - |
| 8.5714 | 150 | 4.517 | - | - |
| 8.9714 | 157 | - | 4.2891 | - |
| 9.1429 | 160 | 4.4587 | - | - |
| 9.7143 | 170 | 4.4879 | - | - |
| 10.0 | 175 | - | 4.2755 | - |
| 10.2857 | 180 | 4.4625 | - | - |
| 10.8571 | 190 | 4.489 | - | - |
| 10.9714 | 192 | - | 4.2716 | - |
| 11.4286 | 200 | 4.4693 | - | - |
| **11.6571** | **204** | **-** | **4.2713** | **0.9836** |
* 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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