Create README.md

README.md

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+---
+base_model: FacebookAI/xlm-roberta-large-finetuned-conll03-english
+tags:
+- generated_from_trainer
+datasets:
+- conll2002
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: KPOETA/BERTO-LOS-MUCHACHOS-1
+  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: 0.880600409370025
+    - name: Recall
+      type: recall
+      value: 0.8897058823529411
+    - name: F1
+      type: f1
+      value: 0.8851297291118985
+    - name: Accuracy
+      type: accuracy
+      value: 0.9806463992982264
+---
+
+<!-- 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. -->
+
+# xml-roberta-large-finetuned-ner
+
+Este es modelo resultado de un finetuning de 
+[FacebookAI/xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english) sobre el conll2002 dataset.
+Los siguientes son los resultados sobre el conjunto de evaluación:
+- Loss: 0.092
+- Precision: 0.8768651513038626
+- Recall: 0.8833942118572633
+- F1: 0.8768651513038628
+- Accuracy: 0.982701988941157
+
+## Model description
+
+Este es el modelo más grande de roberta [FacebookAI/xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english)-
+Este modelo fue ajustado usando el framework Kaggle [https://www.kaggle.com/settings]. Para realizar el preentrenamiento del modelo se tuvo que crear un directorio temporal en Kaggle
+con el fin de almacenar de manera temoporal el modelo que pesa alrededor de 35 Gz.
+
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 4
+- eval_batch_size: 8
+- 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0743        | 1.0   | 2081  | 0.1131          | 0.8385    | 0.8587 | 0.8485 | 0.9771   |
+| 0.049         | 2.0   | 4162  | 0.1429          | 0.8492    | 0.8564 | 0.8528 | 0.9756   |
+| 0.031         | 3.0   | 6243  | 0.1298          | 0.8758    | 0.8817 | 0.8787 | 0.9800   |
+| 0.0185        | 4.0   | 8324  | 0.1279          | 0.8827    | 0.8890 | 0.8859 | 0.9808   |
+| 0.0125        | 5.0   | 10405 | 0.1364          | 0.8806    | 0.8897 | 0.8851 | 0.9806   |