---
language: 
- multilingual
- en 
- ar 
- bg 
- de 
- el 
- es 
- fr 
- hi 
- ru 
- sw 
- th 
- tr 
- ur 
- vi 
- zh
license: mit
tags:
- zero-shot-classification
- text-classification
- nli
- pytorch
metrics:
- accuracy
datasets:
- multi_nli
- xnli
pipeline_tag: zero-shot-classification
widget:
- text: "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
  candidate_labels: "politics, economy, entertainment, environment"
---


---
# Multilingual XLM-V-base-mnli-xnli
## Model description
This multilingual model can perform natural language inference (NLI) on 116 languages and is therefore also 
suitable for multilingual zero-shot classification. The underlying XLM-V-base model was created 
by Meta AI and pretrained on the [CC100 multilingual dataset](https://huggingface.co/datasets/cc100). 
It was then fine-tuned on the [XNLI dataset](https://huggingface.co/datasets/xnli), which contains hypothesis-premise pairs from 15 languages, 
as well as the English [MNLI dataset](https://huggingface.co/datasets/multi_nli).
XLM-V-base was publish on 23.01.2023 in [this paper](https://arxiv.org/pdf/2301.10472.pdf). 
Its main innovation is a larger and better vocabulary: previous multilingual models had a vocabulary of 250 000 tokens, 
while XLM-V 'knows' 1 million tokens. The improved vocabulary allows for better representations of more languages. 


### How to use the model
#### Simple zero-shot classification pipeline
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="MoritzLaurer/xlm-v-base-mnli-xnli")

sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
candidate_labels = ["politics", "economy", "entertainment", "environment"]
output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
print(output)
```
#### NLI use-case
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

model_name = "MoritzLaurer/xlm-v-base-mnli-xnli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

premise = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
hypothesis = "Emmanuel Macron is the President of France"

input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device))  # device = "cuda:0" or "cpu"
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "neutral", "contradiction"]
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
print(prediction)
```

### Training data
This model was trained on the XNLI development dataset and the MNLI train dataset. 
The XNLI development set consists of 2490 professionally translated texts from English 
to 14 other languages (37350 texts in total) (see [this paper](https://arxiv.org/pdf/1809.05053.pdf)). 
Note that the XNLI contains a training set of 15 machine translated versions of the MNLI dataset for 15 languages, 
but due to quality issues with these machine translations, this model was only trained on the professional translations 
from the XNLI development set and the original English MNLI training set (392 702 texts). 
Not using machine translated texts can avoid overfitting the model to the 15 languages; 
avoids catastrophic forgetting of the other 101~ languages XLM-V was pre-trained on; 
and significantly reduces training costs. 

### Training procedure
xlm-v-base-mnli-xnli was trained using the Hugging Face trainer with the following hyperparameters.
```
training_args = TrainingArguments(
    num_train_epochs=3,              # total number of training epochs
    learning_rate=2e-05,
    per_device_train_batch_size=32,   # batch size per device during training
    per_device_eval_batch_size=120,    # batch size for evaluation
    warmup_ratio=0.06,                # number of warmup steps for learning rate scheduler
    weight_decay=0.01,               # strength of weight decay
)
```

### Eval results
The model was evaluated on the XNLI test set on 15 languages (5010 texts per language, 75150 in total). 
Note that multilingual NLI models are capable of classifying NLI texts without receiving NLI training data 
in the specific language (cross-lingual transfer). This means that the model is also able of doing NLI on 
the other 101~ languages XLM-V was training on, but performance is most likely lower than for those languages available in XNLI.

Also note that if other multilingual models on the model hub claim performance of around 90% on languages other than English, 
the authors have most likely made a mistake during testing since non of the latest papers (of mostly larger models) shows a multilingual average performance 
of more than a few points above 80% on XNLI (see [here](https://arxiv.org/pdf/2111.09543.pdf) or [here](https://arxiv.org/pdf/1911.02116.pdf)). 

The average XNLI performance of XLM-V reported in the paper is 0.76 ([see table 2](https://arxiv.org/pdf/2301.10472.pdf)). 
This reimplementation has an average performance of 0.78. 
This increase in performance is probably thanks to the addition of MNLI in the training data. 
Note that [mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) has an average 
performance of 0.808 and is smaller (3GB for XLM-V vs. 560MB for mDeBERTa) and is faster (thanks to mDeBERTa's smaller vocabulary). 
This difference comes probably from mDeBERTa-v3's improved pre-training objective. 
Depending on the task, it is probably better to use [mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli),
but XLM-V could be better on some languages based on its improved vocabulary.

|Datasets|average|ar|bg|de|el|en|es|fr|hi|ru|sw|th|tr|ur|vi|zh|
| :---:  | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---: |
|Accuracy|0.780|0.757|0.808|0.796|0.79|0.856|0.814|0.806|0.751|0.782|0.725|0.757|0.766|0.729|0.784|0.782|
|Speed GPU A100 (text/sec)|na|3501.0|3324.0|3438.0|3174.0|3713.0|3500.0|3129.0|3042.0|3419.0|3468.0|3782.0|3772.0|3099.0|3117.0|4217.0|

|Datasets|mnli_m (en)|mnli_mm (en)|
| :---: | :---: | :---: | 
|Accuracy|0.852|0.854|
|Speed GPU A100 (text/sec)|2098.0|2170.0|


## Limitations and bias
Please consult the original XLM-V paper and literature on different NLI datasets for potential biases. 

## Citation
If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. 
‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. 
Preprint, June. Open Science Framework. https://osf.io/74b8k.

## Ideas for cooperation or questions?
If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)