Training complete

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-finetuned-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: 0.8406680207628074
+    - name: Recall
+      type: recall
+      value: 0.8559283088235294
+    - name: F1
+      type: f1
+      value: 0.8482295343276784
+    - name: Accuracy
+      type: accuracy
+      value: 0.9701989833870568
+---
+
+<!-- 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-finetuned-ner
+
+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: 0.2247
+- Precision: 0.8407
+- Recall: 0.8559
+- F1: 0.8482
+- Accuracy: 0.9702
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0512        | 1.0   | 521  | 0.1314          | 0.8328    | 0.8562 | 0.8443 | 0.9703   |
+| 0.0305        | 2.0   | 1042 | 0.1549          | 0.8320    | 0.8442 | 0.8380 | 0.9688   |
+| 0.0193        | 3.0   | 1563 | 0.1498          | 0.8515    | 0.8580 | 0.8548 | 0.9708   |
+| 0.0148        | 4.0   | 2084 | 0.1809          | 0.8374    | 0.8447 | 0.8410 | 0.9682   |
+| 0.0112        | 5.0   | 2605 | 0.1900          | 0.8391    | 0.8518 | 0.8454 | 0.9702   |
+| 0.0078        | 6.0   | 3126 | 0.1839          | 0.8361    | 0.8545 | 0.8452 | 0.9707   |
+| 0.0058        | 7.0   | 3647 | 0.2060          | 0.8428    | 0.8534 | 0.8480 | 0.9702   |
+| 0.0049        | 8.0   | 4168 | 0.2111          | 0.8334    | 0.8527 | 0.8429 | 0.9697   |
+| 0.0037        | 9.0   | 4689 | 0.2252          | 0.8360    | 0.8502 | 0.8430 | 0.9692   |
+| 0.0031        | 10.0  | 5210 | 0.2247          | 0.8407    | 0.8559 | 0.8482 | 0.9702   |
+
+
+### Framework versions
+
+- Transformers 4.41.0
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1