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README.md
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README.md
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@ -10,16 +10,16 @@ tags:
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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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- xlni
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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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# camembert-base-xlni
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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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Camembert-base model fine-tuned on french part of XLNI 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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@ -46,7 +46,7 @@ classifier(sequence, candidate_labels, hypothesis_template=hypothesis_template)
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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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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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@ -70,7 +70,7 @@ prob_label_is_true[0].tolist() * 100
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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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Training data is the french fold of the [XLNI](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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@ -86,7 +86,6 @@ Main training parameters :
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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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config.json
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config.json
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@ -11,16 +11,16 @@
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "entailment",
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"0": "contradiction",
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"1": "neutral",
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"2": "contradiction"
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"2": "entailment"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"entailment": 0,
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"contradiction": 0,
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"neutral": 1,
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"contradiction": 2
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"entailment": 2
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"output_past": true,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"transformers_version": "4.3.3",
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"transformers_version": "4.4.2",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 32005
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tf_model.h5 (Stored with Git LFS)
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tf_model.h5 (Stored with Git LFS)
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