Training complete with metrics

README.md

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+---
+license: cc-by-4.0
+base_model: NazaGara/NER-fine-tuned-BETO
+tags:
+- generated_from_trainer
+datasets:
+- conll2002
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: beto-base-cased-finetuned-conll2002
+  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: 0.8607856650585803
+    - name: Recall
+      type: recall
+      value: 0.8609834558823529
+    - name: F1
+      type: f1
+      value: 0.860884549109707
+    - name: Accuracy
+      type: accuracy
+      value: 0.9786860155319993
+---
 
-# BETO Base Cased finetuned on CONLL2002
+<!-- 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. -->
+
+# beto-base-cased-finetuned-conll2002
+
+This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the conll2002 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1281
+- Precision: 0.8608
+- Recall: 0.8610
+- F1: 0.8609
+- Accuracy: 0.9787
 
 ## Model description
-This is a BETO base cased model fine-tuned on the CONLL2002 dataset for Named Entity Recognition (NER).
 
-## Metrics
-- Precision: 0.8607856650585803
-- Recall: 0.8609834558823529
-- F1-score: 0.860884549109707
-- Accuracy: 0.9786860155319993
+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: 8
+- eval_batch_size: 8
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.019         | 1.0   | 1041 | 0.1095          | 0.8581    | 0.8628 | 0.8604 | 0.9790   |
+| 0.018         | 2.0   | 2082 | 0.1121          | 0.8461    | 0.8589 | 0.8525 | 0.9783   |
+| 0.0133        | 3.0   | 3123 | 0.1281          | 0.8608    | 0.8610 | 0.8609 | 0.9787   |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

model.safetensors

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