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update model card README.md
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README.md
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---
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language:
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- mn
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license: mit
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: roberta-large-mnli-ner-2000
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results: []
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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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# roberta-large-mnli-ner-2000
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This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2962
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- Precision: 0.5550
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- Recall: 0.7002
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- F1: 0.6192
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- Accuracy: 0.9229
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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: 16
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.5957 | 1.0 | 47 | 0.3873 | 0.3785 | 0.5503 | 0.4485 | 0.8762 |
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| 0.3783 | 2.0 | 94 | 0.3326 | 0.4809 | 0.6208 | 0.5420 | 0.8970 |
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| 0.31 | 3.0 | 141 | 0.3072 | 0.4149 | 0.5996 | 0.4904 | 0.8932 |
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| 0.2706 | 4.0 | 188 | 0.2973 | 0.5096 | 0.6510 | 0.5717 | 0.9096 |
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| 0.2486 | 5.0 | 235 | 0.3273 | 0.4987 | 0.6454 | 0.5627 | 0.9061 |
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| 0.2113 | 6.0 | 282 | 0.2658 | 0.5148 | 0.6611 | 0.5788 | 0.9146 |
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| 0.1856 | 7.0 | 329 | 0.2824 | 0.5140 | 0.6767 | 0.5843 | 0.9138 |
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| 0.1554 | 8.0 | 376 | 0.2944 | 0.5450 | 0.6980 | 0.6121 | 0.9181 |
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| 0.1362 | 9.0 | 423 | 0.2893 | 0.5475 | 0.6969 | 0.6132 | 0.9199 |
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| 0.1232 | 10.0 | 470 | 0.2962 | 0.5550 | 0.7002 | 0.6192 | 0.9229 |
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### Framework versions
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- Transformers 4.28.1
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- Pytorch 2.0.0+cu118
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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