Model save

README.md

@@ -0,0 +1,92 @@
+---
+license: apache-2.0
+base_model: BSC-LT/roberta-base-bne-capitel-ner
+tags:
+- generated_from_trainer
+datasets:
+- conll2002
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: roberta-base-bne-capitel-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: 0.8601446000903751
+    - name: Recall
+      type: recall
+      value: 0.8747702205882353
+    - name: F1
+      type: f1
+      value: 0.8673957621326043
+    - name: Accuracy
+      type: accuracy
+      value: 0.9779993282237626
+---
+
+<!-- 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. -->
+
+# roberta-base-bne-capitel-ner
+
+This model is a fine-tuned version of [BSC-LT/roberta-base-bne-capitel-ner](https://huggingface.co/BSC-LT/roberta-base-bne-capitel-ner) on the conll2002 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1229
+- Precision: 0.8601
+- Recall: 0.8748
+- F1: 0.8674
+- Accuracy: 0.9780
+
+## 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: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0172        | 1.0   | 1041 | 0.1157          | 0.8468    | 0.8640 | 0.8553 | 0.9770   |
+| 0.0109        | 2.0   | 2082 | 0.1177          | 0.8705    | 0.8853 | 0.8779 | 0.9786   |
+| 0.0066        | 3.0   | 3123 | 0.1229          | 0.8601    | 0.8748 | 0.8674 | 0.9780   |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.0.1+cu117
+- Datasets 2.14.4
+- Tokenizers 0.14.1