End of training

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: NER-finetuning-BETO-PRO
+  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: 0.8488716662867564
+    - name: Recall
+      type: recall
+      value: 0.8556985294117647
+    - name: F1
+      type: f1
+      value: 0.8522714269367205
+    - name: Accuracy
+      type: accuracy
+      value: 0.969672080337218
+---
+
+<!-- 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. -->
+
+# NER-finetuning-BETO-PRO
+
+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: 0.2388
+- Precision: 0.8489
+- Recall: 0.8557
+- F1: 0.8523
+- Accuracy: 0.9697
+
+## 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: 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0507        | 1.0   | 1041  | 0.1411          | 0.8326    | 0.8536 | 0.8430 | 0.9707   |
+| 0.0308        | 2.0   | 2082  | 0.1721          | 0.8263    | 0.8405 | 0.8334 | 0.9679   |
+| 0.0205        | 3.0   | 3123  | 0.1766          | 0.8446    | 0.8516 | 0.8481 | 0.9692   |
+| 0.0139        | 4.0   | 4164  | 0.2043          | 0.8422    | 0.8460 | 0.8441 | 0.9684   |
+| 0.0127        | 5.0   | 5205  | 0.1907          | 0.8414    | 0.8548 | 0.8481 | 0.9698   |
+| 0.0084        | 6.0   | 6246  | 0.2069          | 0.8427    | 0.8470 | 0.8448 | 0.9696   |
+| 0.0056        | 7.0   | 7287  | 0.2275          | 0.8533    | 0.8610 | 0.8571 | 0.9700   |
+| 0.0044        | 8.0   | 8328  | 0.2307          | 0.8408    | 0.8534 | 0.8471 | 0.9698   |
+| 0.0026        | 9.0   | 9369  | 0.2343          | 0.8469    | 0.8504 | 0.8487 | 0.9695   |
+| 0.0024        | 10.0  | 10410 | 0.2388          | 0.8489    | 0.8557 | 0.8523 | 0.9697   |
+
+
+### Framework versions
+
+- Transformers 4.44.0
+- Pytorch 2.4.0
+- Datasets 2.20.0
+- Tokenizers 0.19.1