language	thumbnail	tags	license	pipeline_tag	datasets	metrics
fr		zero-shot-classification xnli nli fr	mit	zero-shot-classification	xnli	accuracy

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 \( P(premise|hypothesis ) \)

# 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