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: roberta-base-mapa_fine-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: lextreme
+      type: lextreme
+      config: mapa_fine
+      split: test
+      args: mapa_fine
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7395134779750164
+    - name: Recall
+      type: recall
+      value: 0.8236672524897481
+    - name: F1
+      type: f1
+      value: 0.7793251576248873
+    - name: Accuracy
+      type: accuracy
+      value: 0.991740752278482
+---
+
+<!-- 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. -->
+
+# roberta-base-mapa_fine-ner
+
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the lextreme dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0401
+- Precision: 0.7395
+- Recall: 0.8237
+- F1: 0.7793
+- Accuracy: 0.9917
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0877        | 1.0   | 1739  | 0.0495          | 0.6861    | 0.7595 | 0.7209 | 0.9903   |
+| 0.0661        | 2.0   | 3478  | 0.0432          | 0.7278    | 0.8092 | 0.7663 | 0.9914   |
+| 0.0633        | 3.0   | 5217  | 0.0403          | 0.7469    | 0.8128 | 0.7785 | 0.9919   |
+| 0.059         | 4.0   | 6956  | 0.0401          | 0.7412    | 0.8196 | 0.7784 | 0.9918   |
+| 0.063         | 5.0   | 8695  | 0.0400          | 0.7425    | 0.8200 | 0.7793 | 0.9918   |
+| 0.0593        | 6.0   | 10434 | 0.0405          | 0.7332    | 0.8244 | 0.7761 | 0.9916   |
+| 0.0595        | 7.0   | 12173 | 0.0400          | 0.7389    | 0.8222 | 0.7783 | 0.9917   |
+| 0.0593        | 8.0   | 13912 | 0.0401          | 0.7390    | 0.8229 | 0.7787 | 0.9917   |
+| 0.0594        | 9.0   | 15651 | 0.0402          | 0.7374    | 0.8240 | 0.7783 | 0.9917   |
+| 0.0597        | 10.0  | 17390 | 0.0401          | 0.7395    | 0.8237 | 0.7793 | 0.9917   |
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.13.1+cu117
+- Datasets 2.9.0
+- Tokenizers 0.13.2