---
pipeline_tag: sentence-similarity
language: "multilingual"
tags:
- feature-extraction
- sentence-similarity
- transformers
- multilingual
---

# mstsb-paraphrase-multilingual-mpnet-base-v2

This is a fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` from [sentence-transformers](https://www.SBERT.net) model with [Semantic Textual Similarity Benchmark](http://ixa2.si.ehu.eus/stswiki/index.php/Main_Page) extended to 15 languages: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering, semantic search and measuring the similarity between two sentences.

<!--- Describe your model here -->
This model is fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` for semantic textual similarity with multilingual data. The dataset used for this fine-tuning is STSb extended to 15 languages with Google Translator. For mantaining data quality the sentence pairs with a confidence value below 0.7 were dropped. The extended dataset is available at [GitHub](https://github.com/Huertas97/Multilingual-STSB). The languages included in the extended version are: ar, cs, de, en, es, fr, hi, it, ja, nl, pl, pt, ru, tr, zh-CN, zh-TW. The pooling operation used to condense the word embeddings into a sentence embedding is mean pooling (more info below). 

<!-- ## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
# It support several languages
sentences = ["This is an example sentence", "Esta es otra frase de ejemplo", "最後の例文"]

# The pooling technique is automatically detected (mean pooling)
model = SentenceTransformer('mstsb-paraphrase-multilingual-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
``` -->



## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch

# We should define the proper pooling function: Mean pooling
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ["This is an example sentence", "Esta es otra frase de ejemplo", "最後の例文"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2')
model = AutoModel.from_pretrained('AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```



## Evaluation Results

<!--- Describe how your model was evaluated -->
Check the test results in the Semantic Textual Similarity Tasks. The 15 languages available at the [Multilingual STSB](https://github.com/Huertas97/Multilingual-STSB) have been combined into monolingual and cross-lingual tasks, giving a total of 31 tasks. Monolingual tasks have both sentences from the same language source (e.g., Ar-Ar, Es-Es), while cross-lingual tasks have two sentences, each in a different language being one of them English (e.g., en-ar, en-es). For the sake of readability tasks have been splitted into monolingual and cross-lingual tasks. 

| Monolingual Task | Pearson Cosine test | Spearman Cosine  test |
|------------------|---------------------|-----------------------|
| en;en            | 0.868048310692506   | 0.8740170943535747    |
| ar;ar            | 0.8267139454193487  | 0.8284459741532022    |
| cs;cs            | 0.8466821720942157  | 0.8485417688803879    |
| de;de            | 0.8517285961812183  | 0.8557680051557893    |
| es;es            | 0.8519185309064691  | 0.8552243211580456    |
| fr;fr            | 0.8430951067985064  | 0.8466614534379704    |
| hi;hi            | 0.8178258630578092  | 0.8176462079184331    |
| it;it            | 0.8475909574305637  | 0.8494216064459076    |
| ja;ja            | 0.8435588859386477  | 0.8456031494178619    |
| nl;nl            | 0.8486765104527032  | 0.8520856765262531    |
| pl;pl            | 0.8407840177883407  | 0.8443070467300299    |
| pt;pt            | 0.8534880178249296  | 0.8578544068829622    |
| ru;ru            | 0.8390897585455678  | 0.8423041443534423    |
| tr;tr            | 0.8382125451820572  | 0.8421587450058385    |
| zh-CN;zh-CN      | 0.826233678946644   | 0.8248515460782744    |
| zh-TW;zh-TW      | 0.8242683809675422  | 0.8235506799952028    |

\\

| Cross-lingual Task | Pearson Cosine test | Spearman Cosine  test |
|--------------------|---------------------|-----------------------|
| en;ar              | 0.7990830340462535  | 0.7956792016468148    |
| en;cs              | 0.8381274879061265  | 0.8388713450024455    |
| en;de              | 0.8414439600928739  | 0.8441971698649943    |
| en;es              | 0.8442337511356952  | 0.8445035292903559    |
| en;fr              | 0.8378437644605063  | 0.8387903367907733    |
| en;hi              | 0.7951955086055527  | 0.7905052217683244    |
| en;it              | 0.8415686372978766  | 0.8419480899107785    |
| en;ja              | 0.8094306665283388  | 0.8032512280936449    |
| en;nl              | 0.8389526140129767  | 0.8409310421803277    |
| en;pl              | 0.8261309163979578  | 0.825976253023656     |
| en;pt              | 0.8475546209070765  | 0.8506606391790897    |
| en;ru              | 0.8248514914263723  | 0.8224871183202255    |
| en;tr              | 0.8191803661207868  | 0.8194200775744044    |
| en;zh-CN           | 0.8147678083378249  | 0.8102089470690433    |
| en;zh-TW           | 0.8107272160374955  | 0.8056129680510944    |


## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 687 with parameters:
```
{'batch_size': 132, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 

Parameters of the fit()-Method:
```
{
    "callback": null,
    "epochs": 2,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 140,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors

<!--- Describe where people can find more information -->