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update model card README.md

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+ ---
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+ license: agpl-3.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - mim_gold_ner
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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: XLMR-ENIS-finetuned-ner
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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: mim_gold_ner
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+ type: mim_gold_ner
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+ args: mim-gold-ner
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.8666203542896839
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+ - name: Recall
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+ type: recall
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+ value: 0.8510517339397385
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+ - name: F1
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+ type: f1
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+ value: 0.8587654887563103
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9833747693058585
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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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+ # XLMR-ENIS-finetuned-ner
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+
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+ This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the mim_gold_ner dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0907
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+ - Precision: 0.8666
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+ - Recall: 0.8511
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+ - F1: 0.8588
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+ - Accuracy: 0.9834
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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: 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 | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0573 | 1.0 | 2904 | 0.0961 | 0.8543 | 0.8134 | 0.8334 | 0.9806 |
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+ | 0.0314 | 2.0 | 5808 | 0.0912 | 0.8709 | 0.8282 | 0.8490 | 0.9819 |
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+ | 0.0203 | 3.0 | 8712 | 0.0907 | 0.8666 | 0.8511 | 0.8588 | 0.9834 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.11.2
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+ - Pytorch 1.9.0+cu102
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+ - Datasets 1.12.1
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+ - Tokenizers 0.10.3