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update model card README.md

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+ ---
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+ license: mit
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: mnli_IndE
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+ results: []
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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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+ # mnli_IndE
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+
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+ This model is a fine-tuned version of [WillHeld/roberta-base-mnli](https://huggingface.co/WillHeld/roberta-base-mnli) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5406
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+ - Acc: 0.8536
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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: 2e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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: 3
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Acc |
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+ |:-------------:|:-----:|:-----:|:---------------:|:------:|
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+ | 0.3901 | 0.17 | 2000 | 0.4456 | 0.8354 |
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+ | 0.3758 | 0.33 | 4000 | 0.4508 | 0.8356 |
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+ | 0.3668 | 0.5 | 6000 | 0.4372 | 0.8425 |
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+ | 0.3653 | 0.67 | 8000 | 0.4357 | 0.8400 |
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+ | 0.3543 | 0.83 | 10000 | 0.4030 | 0.8517 |
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+ | 0.3559 | 1.0 | 12000 | 0.4242 | 0.8472 |
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+ | 0.2523 | 1.17 | 14000 | 0.4746 | 0.8464 |
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+ | 0.2521 | 1.33 | 16000 | 0.4780 | 0.8470 |
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+ | 0.2525 | 1.5 | 18000 | 0.4664 | 0.8507 |
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+ | 0.2464 | 1.67 | 20000 | 0.4806 | 0.8484 |
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+ | 0.2495 | 1.83 | 22000 | 0.4868 | 0.8464 |
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+ | 0.2451 | 2.0 | 24000 | 0.4794 | 0.8508 |
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+ | 0.1737 | 2.17 | 26000 | 0.5492 | 0.8491 |
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+ | 0.1727 | 2.33 | 28000 | 0.5552 | 0.8531 |
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+ | 0.1736 | 2.5 | 30000 | 0.5418 | 0.8515 |
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+ | 0.1746 | 2.67 | 32000 | 0.5511 | 0.8516 |
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+ | 0.1717 | 2.83 | 34000 | 0.5406 | 0.8536 |
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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+cu117
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+ - Datasets 2.7.1
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+ - Tokenizers 0.12.1