update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- conll2002
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: Plant-gob-roberta-base-bne-capitel-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2002
+      type: conll2002
+      config: es
+      split: validation
+      args: es
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8627583108715184
+    - name: Recall
+      type: recall
+      value: 0.8825827205882353
+    - name: F1
+      type: f1
+      value: 0.872557928214448
+    - name: Accuracy
+      type: accuracy
+      value: 0.9784878927600843
+---
+
+<!-- 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. -->
+
+# Plant-gob-roberta-base-bne-capitel-ner
+
+This model is a fine-tuned version of [BSC-LT/roberta-base-bne-capitel-ner](https://huggingface.co/BSC-LT/roberta-base-bne-capitel-ner) on the conll2002 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1071
+- Precision: 0.8628
+- Recall: 0.8826
+- F1: 0.8726
+- Accuracy: 0.9785
+
+## 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0681        | 1.0   | 521  | 0.0818          | 0.8645    | 0.8842 | 0.8742 | 0.9789   |
+| 0.0313        | 2.0   | 1042 | 0.0841          | 0.8516    | 0.8768 | 0.8640 | 0.9779   |
+| 0.0179        | 3.0   | 1563 | 0.0979          | 0.8584    | 0.8773 | 0.8677 | 0.9781   |
+| 0.0091        | 4.0   | 2084 | 0.0996          | 0.8600    | 0.8819 | 0.8708 | 0.9784   |
+| 0.0068        | 5.0   | 2605 | 0.1071          | 0.8628    | 0.8826 | 0.8726 | 0.9785   |
+
+
+### Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3