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update model card README.md

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+ ---
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+ license: apache-2.0
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+ tags:
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+ - text-classification
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+ - generated_from_trainer
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+ datasets:
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+ - xnli
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: xnli_m_bert_only_fr
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: xnli
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+ type: xnli
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+ config: fr
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+ split: train
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+ args: fr
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.7674698795180723
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # xnli_m_bert_only_fr
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+
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+ This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the xnli dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.2262
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+ - Accuracy: 0.7675
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 128
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+ - eval_batch_size: 128
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 0.6184 | 1.0 | 3068 | 0.6251 | 0.7373 |
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+ | 0.5293 | 2.0 | 6136 | 0.5669 | 0.7635 |
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+ | 0.4343 | 3.0 | 9204 | 0.6161 | 0.7651 |
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+ | 0.3456 | 4.0 | 12272 | 0.6650 | 0.7631 |
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+ | 0.2677 | 5.0 | 15340 | 0.7249 | 0.7755 |
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+ | 0.2022 | 6.0 | 18408 | 0.8638 | 0.7590 |
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+ | 0.1488 | 7.0 | 21476 | 0.9073 | 0.7763 |
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+ | 0.1096 | 8.0 | 24544 | 1.0603 | 0.7586 |
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+ | 0.0813 | 9.0 | 27612 | 1.1546 | 0.7687 |
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+ | 0.0599 | 10.0 | 30680 | 1.2262 | 0.7675 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.24.0
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+ - Pytorch 1.13.0
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+ - Datasets 2.6.1
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+ - Tokenizers 0.13.1