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# camembert-base-xnli

## Model description

Camembert-base model fine-tuned on french part of XNLI dataset. One of the few Zero-Shot classification model working on French 🇫🇷

## Intended uses & limitations

#### How to use

Two different usages :

- As a Zero-Shot sequence classifier :

```
classifier = pipeline("zero-shot-classification",
model="BaptisteDoyen/camembert-base-xnli")
sequence = "L'équipe de France joue aujourd'hui au Parc des Princes"
candidate_labels = ["sport","politique","science"]
hypothesis_template = "Ce texte parle de {}."
classifier(sequence, candidate_labels, hypothesis_template=hypothesis_template)
# outputs :
# {'sequence': "L'équipe de France joue aujourd'hui au Parc des Princes",
# 'labels': ['sport', 'politique', 'science'],
# 'scores': [0.8595073223114014, 0.10821866989135742, 0.0322740375995636]}
```

- As a premise/hypothesis checker : The idea is here to compute a probability of the form P(premise∣hypothesis) P(premise|hypothesis ) P(premise∣hypothesis)

```
# load model and tokenizer
nli_model = AutoModelForSequenceClassification.from_pretrained("BaptisteDoyen/camembert-base-xnli")
tokenizer = AutoTokenizer.from_pretrained("BaptisteDoyen/camembert-base-xnli")
# sequences
premise = "le score pour les bleus est élevé"
hypothesis = "L'équipe de France a fait un bon match"
# tokenize and run through model
x = tokenizer.encode(premise, hypothesis, return_tensors='pt')
logits = nli_model(x)[0]
# we throw away "neutral" (dim 1) and take the probability of
# "entailment" (0) as the probability of the label being true
entail_contradiction_logits = logits[:,::2]
probs = entail_contradiction_logits.softmax(dim=1)
prob_label_is_true = probs[:,0]
prob_label_is_true[0].tolist() * 100
# outputs
# 86.40775084495544
```

## Training data

Training data is the french fold of the XNLI dataset released in 2018 by Facebook. Available with great ease using the datasets library :

```
from datasets import load_dataset
dataset = load_dataset('xnli', 'fr')
```

## Training/Fine-Tuning procedure

Training procedure is here pretty basic and was performed on the cloud using a single GPU. Main training parameters :

`lr = 2e-5 with lr_scheduler_type = "linear"`

`num_train_epochs = 4`

`batch_size = 12 (limited by GPU-memory)`

`weight_decay = 0.01`

`metric_for_best_model = "eval_accuracy"`

## Eval results

We obtain the following results on validation and test sets:

Set | Accuracy |
---|---|

validation | 81.4 |

test | 81.7 |

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