update model card README.md

README.md

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+---
+license: agpl-3.0
+tags:
+- generated_from_trainer
+datasets:
+- mim_gold_ner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: XLMR-ENIS-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: mim_gold_ner
+      type: mim_gold_ner
+      args: mim-gold-ner
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8713799976550592
+    - name: Recall
+      type: recall
+      value: 0.8450255827174531
+    - name: F1
+      type: f1
+      value: 0.8580004617871162
+    - name: Accuracy
+      type: accuracy
+      value: 0.9827265378338392
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# XLMR-ENIS-finetuned-ner
+
+This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the mim_gold_ner dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0941
+- Precision: 0.8714
+- Recall: 0.8450
+- F1: 0.8580
+- Accuracy: 0.9827
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0572        | 1.0   | 2904 | 0.0998          | 0.8586    | 0.8171 | 0.8373 | 0.9802   |
+| 0.0313        | 2.0   | 5808 | 0.0868          | 0.8666    | 0.8288 | 0.8473 | 0.9822   |
+| 0.0199        | 3.0   | 8712 | 0.0941          | 0.8714    | 0.8450 | 0.8580 | 0.9827   |
+
+
+### Framework versions
+
+- Transformers 4.11.2
+- Pytorch 1.9.0+cu102
+- Datasets 1.12.1
+- Tokenizers 0.10.3