99 lines
2.9 KiB
Markdown
99 lines
2.9 KiB
Markdown
---
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language:
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- fr
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thumbnail:
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tags:
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- zero-shot-classification
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- xnli
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- nli
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- fr
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license: mit
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pipeline_tag: zero-shot-classification
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datasets:
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- xnli
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metrics:
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- accuracy
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---
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# camembert-base-xnli
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## Model description
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Camembert-base model fine-tuned on french part of XNLI dataset. <br>
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One of the few Zero-Shot classification model working on french 🇫🇷
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## Intended uses & limitations
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#### How to use
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Two different usages :
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- As a Zero-Shot sequence classifier :
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```python
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classifier = pipeline("zero-shot-classification",
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model="BaptisteDoyen/camembert-base-xnli")
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sequence = "L'équipe de France joue aujourd'hui au Parc des Princes"
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candidate_labels = ["sport","politique","science"]
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hypothesis_template = "Ce texte parle de {}."
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classifier(sequence, candidate_labels, hypothesis_template=hypothesis_template)
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# outputs :
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# {'sequence': "L'équipe de France joue aujourd'hui au Parc des Princes",
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# 'labels': ['sport', 'politique', 'science'],
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# 'scores': [0.8595073223114014, 0.10821866989135742, 0.0322740375995636]}
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```
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- As a premise/hypothesis checker : <br>
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The idea is here to compute a probability of the form \\( P(premise|hypothesis ) \\)
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```python
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# load model and tokenizer
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nli_model = AutoModelForSequenceClassification.from_pretrained("BaptisteDoyen/camembert-base-xnli")
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tokenizer = AutoTokenizer.from_pretrained("BaptisteDoyen/camembert-base-xnli")
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# sequences
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premise = "le score pour les bleus est élevé"
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hypothesis = "L'équipe de France a fait un bon match"
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# tokenize and run through model
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x = tokenizer.encode(premise, hypothesis, return_tensors='pt')
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logits = nli_model(x)[0]
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# we throw away "neutral" (dim 1) and take the probability of
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# "entailment" (0) as the probability of the label being true
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entail_contradiction_logits = logits[:,::2]
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probs = entail_contradiction_logits.softmax(dim=1)
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prob_label_is_true = probs[:,0]
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prob_label_is_true[0].tolist() * 100
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# outputs
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# 86.40775084495544
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```
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## Training data
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Training data is the french fold of the [XNLI](https://research.fb.com/publications/xnli-evaluating-cross-lingual-sentence-representations/) dataset released in 2018 by Facebook. <br>
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Available with great ease using the ```datasets``` library :
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```python
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from datasets import load_dataset
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dataset = load_dataset('xnli', 'fr')
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```
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## Training/Fine-Tuning procedure
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Training procedure is here pretty basic and was performed on the cloud using a single GPU. <br>
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Main training parameters :
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- ```lr = 2e-5``` with ```lr_scheduler_type = "linear"```
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- ```num_train_epochs = 4```
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- ```batch_size = 12``` (limited by GPU-memory)
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- ```weight_decay = 0.01```
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- ```metric_for_best_model = "eval_accuracy"```
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## Eval results
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We obtain the following results on ```validation``` and ```test``` sets:
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| Set | Accuracy |
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| ---------- |-------------|
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| validation | 81.4 |
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| test | 81.7 |
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