update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- lextreme
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: xlm-roberta-base-mapa_coarse-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: lextreme
+      type: lextreme
+      config: mapa_coarse
+      split: test
+      args: mapa_coarse
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.6624395127648923
+    - name: Recall
+      type: recall
+      value: 0.6656606304493629
+    - name: F1
+      type: f1
+      value: 0.6640461654261103
+    - name: Accuracy
+      type: accuracy
+      value: 0.9872255987419513
+---
+
+<!-- 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. -->
+
+# xlm-roberta-base-mapa_coarse-ner
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the lextreme dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0515
+- Precision: 0.6624
+- Recall: 0.6657
+- F1: 0.6640
+- Accuracy: 0.9872
+
+## 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0584        | 1.0   | 1739  | 0.0576          | 0.6088    | 0.5790 | 0.5935 | 0.9860   |
+| 0.0475        | 2.0   | 3478  | 0.0522          | 0.6455    | 0.6574 | 0.6514 | 0.9870   |
+| 0.0409        | 3.0   | 5217  | 0.0517          | 0.6490    | 0.6675 | 0.6581 | 0.9871   |
+| 0.04          | 4.0   | 6956  | 0.0516          | 0.6562    | 0.6720 | 0.6640 | 0.9871   |
+| 0.0422        | 5.0   | 8695  | 0.0513          | 0.6573    | 0.6722 | 0.6647 | 0.9871   |
+| 0.0398        | 6.0   | 10434 | 0.0515          | 0.6602    | 0.6697 | 0.6649 | 0.9872   |
+| 0.0407        | 7.0   | 12173 | 0.0516          | 0.6612    | 0.6663 | 0.6638 | 0.9872   |
+| 0.0382        | 8.0   | 13912 | 0.0516          | 0.6626    | 0.6648 | 0.6637 | 0.9872   |
+| 0.0398        | 9.0   | 15651 | 0.0515          | 0.6627    | 0.6660 | 0.6643 | 0.9872   |
+| 0.0401        | 10.0  | 17390 | 0.0515          | 0.6624    | 0.6657 | 0.6640 | 0.9872   |
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.13.1+cu117
+- Datasets 2.9.0
+- Tokenizers 0.13.2