---
datasets:
- xnli
model-index:
- name: multilingual-e5-base-xnli
  results: []
pipeline_tag: zero-shot-classification
language:
- multilingual
- en 
- ar 
- bg 
- de 
- el 
- es 
- fr  
- ru 
- sw 
- th 
- tr 
- ur 
- vi 
- zh
license: mit
widget:
- text: "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
  candidate_labels: "politics, economy, entertainment, environment"
---

# multilingual-e5-base-xnli

This model is a fine-tuned version of [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on the XNLI dataset.

## Model description

[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022

## How to use the model

The model can be loaded with the `zero-shot-classification` pipeline like so:

```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
                      model="mjwong/multilingual-e5-base-xnli")
```

You can then use this pipeline to classify sequences into any of the class names you specify.

```python
sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
candidate_labels = ["politics", "economy", "entertainment", "environment"]
classifier(sequence_to_classify, candidate_labels)
#{'sequence': 'Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU',
# 'labels': ['politics', 'economy', 'environment', 'entertainment'],
# 'scores': [0.5737097859382629,
#  0.22160571813583374,
#  0.121183380484581,
#  0.08350115269422531]}
```

If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently:

```python
candidate_labels = ["politics", "economy", "entertainment", "environment"]
classifier(sequence_to_classify, candidate_labels, multi_label=True)
#{'sequence': 'Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU',
# 'labels': ['politics', 'economy', 'environment', 'entertainment'],
# 'scores': [0.8171144723892212,
#  0.4487208425998688,
#  0.11949186027050018,
#  0.04112538695335388]}
```

### Eval results
The model was evaluated using the XNLI test sets on 14 languages: English (en), Arabic (ar), Bulgarian (bg), German (de), Greek (el), Spanish (es), French (fr), Russian (ru), Swahili (sw), Thai (th), Turkish (tr), Urdu (ur), Vietnam (vi) and Chinese (zh). The metric used is accuracy.

|Datasets|en|ar|bg|de|el|es|fr|ru|sw|th|tr|ur|vi|zh|
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|[multilingual-e5-base-xnli](https://huggingface.co/mjwong/multilingual-e5-base-xnli)|0.849|0.771|0.800|0.796|0.795|0.812|0.801|0.783|0.731|0.767|0.771|0.710|0.789|0.786|
|[multilingual-e5-large-xnli](https://huggingface.co/mjwong/multilingual-e5-large-xnli)|0.867|0.798|0.829|0.821|0.820|0.838|0.828|0.810|0.752|0.787|0.794|0.726|0.804|0.810|

### Training hyperparameters

The following hyperparameters were used during training:

- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1+cu116
- Datasets 2.11.0
- Tokenizers 0.12.1