Training complete - roberta-base-LeNER

README.md

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+---
+license: mit
+base_model: FacebookAI/roberta-base
+tags:
+- generated_from_trainer
+datasets:
+- lener_br
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: roberta-base_LeNER-Br
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: lener_br
+      type: lener_br
+      config: lener_br
+      split: validation
+      args: lener_br
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.765
+    - name: Recall
+      type: recall
+      value: 0.8415841584158416
+    - name: F1
+      type: f1
+      value: 0.8014667365112624
+    - name: Accuracy
+      type: accuracy
+      value: 0.9711736213348917
+---
+
+<!-- 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_LeNER-Br
+
+This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the lener_br dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Precision: 0.765
+- Recall: 0.8416
+- F1: 0.8015
+- Accuracy: 0.9712
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.293         | 1.0   | 979  | nan             | 0.5758    | 0.7525 | 0.6524 | 0.9542   |
+| 0.0596        | 2.0   | 1958 | nan             | 0.6546    | 0.7987 | 0.7195 | 0.9534   |
+| 0.0376        | 3.0   | 2937 | nan             | 0.7366    | 0.8339 | 0.7822 | 0.9672   |
+| 0.0256        | 4.0   | 3916 | nan             | 0.6975    | 0.8042 | 0.7471 | 0.9627   |
+| 0.0192        | 5.0   | 4895 | nan             | 0.7173    | 0.8317 | 0.7702 | 0.9646   |
+| 0.013         | 6.0   | 5874 | nan             | 0.7271    | 0.8498 | 0.7837 | 0.9605   |
+| 0.013         | 7.0   | 6853 | nan             | 0.7426    | 0.8537 | 0.7943 | 0.9680   |
+| 0.0064        | 8.0   | 7832 | nan             | 0.7493    | 0.8399 | 0.7920 | 0.9702   |
+| 0.0052        | 9.0   | 8811 | nan             | 0.7611    | 0.8273 | 0.7928 | 0.9725   |
+| 0.0044        | 10.0  | 9790 | nan             | 0.765     | 0.8416 | 0.8015 | 0.9712   |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1