Training complete

README.md

@@ -0,0 +1,99 @@
+---
+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
+  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: 0.8416742493175614
+    - name: Recall
+      type: recall
+      value: 0.8501838235294118
+    - name: F1
+      type: f1
+      value: 0.8459076360310929
+    - name: Accuracy
+      type: accuracy
+      value: 0.967827919662782
+---
+
+<!-- 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
+
+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: 0.2653
+- Precision: 0.8417
+- Recall: 0.8502
+- F1: 0.8459
+- Accuracy: 0.9678
+
+## 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  | 0.1448          | 0.8298    | 0.8571 | 0.8432 | 0.9691   |
+| 0.0333        | 2.0   | 2082  | 0.1728          | 0.8259    | 0.8481 | 0.8369 | 0.9678   |
+| 0.0195        | 3.0   | 3123  | 0.1722          | 0.8392    | 0.8516 | 0.8453 | 0.9693   |
+| 0.0147        | 4.0   | 4164  | 0.2037          | 0.8502    | 0.8488 | 0.8495 | 0.9679   |
+| 0.011         | 5.0   | 5205  | 0.2041          | 0.8394    | 0.8529 | 0.8461 | 0.9695   |
+| 0.0082        | 6.0   | 6246  | 0.2418          | 0.8410    | 0.8401 | 0.8406 | 0.9664   |
+| 0.006         | 7.0   | 7287  | 0.2323          | 0.8448    | 0.8552 | 0.8500 | 0.9678   |
+| 0.0046        | 8.0   | 8328  | 0.2415          | 0.8411    | 0.8527 | 0.8469 | 0.9691   |
+| 0.003         | 9.0   | 9369  | 0.2502          | 0.8402    | 0.8495 | 0.8448 | 0.9677   |
+| 0.0022        | 10.0  | 10410 | 0.2653          | 0.8417    | 0.8502 | 0.8459 | 0.9678   |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1