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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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+ datasets:
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+ - wikiann
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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_ner_wikiann
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: wikiann
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+ type: wikiann
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+ args: en
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.8462551098177787
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+ - name: Recall
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+ type: recall
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+ value: 0.8634242895518167
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+ - name: F1
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+ type: f1
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+ value: 0.8547534903250638
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9382388000397338
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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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+ # roberta-large_ner_wikiann
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+
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+ This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the wikiann dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2783
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+ - Precision: 0.8463
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+ - Recall: 0.8634
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+ - F1: 0.8548
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+ - Accuracy: 0.9382
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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: 16
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+ - eval_batch_size: 16
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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: cosine
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.3395 | 1.0 | 1250 | 0.2652 | 0.8039 | 0.8308 | 0.8171 | 0.9242 |
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+ | 0.2343 | 2.0 | 2500 | 0.2431 | 0.8354 | 0.8503 | 0.8428 | 0.9329 |
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+ | 0.1721 | 3.0 | 3750 | 0.2315 | 0.8330 | 0.8503 | 0.8416 | 0.9352 |
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+ | 0.1156 | 4.0 | 5000 | 0.2709 | 0.8477 | 0.8634 | 0.8554 | 0.9385 |
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+ | 0.1026 | 5.0 | 6250 | 0.2783 | 0.8463 | 0.8634 | 0.8548 | 0.9382 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.20.1
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+ - Pytorch 1.11.0
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+ - Datasets 2.1.0
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+ - Tokenizers 0.12.1