Training complete - xlm-roberta-base-LeNER

README.md

@@ -0,0 +1,99 @@
+---
+license: mit
+base_model: FacebookAI/xlm-roberta-base
+tags:
+- generated_from_trainer
+datasets:
+- lener_br
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: xlm-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: 0.8295165394402035
+    - name: Recall
+      type: recall
+      value: 0.8965896589658966
+    - name: F1
+      type: f1
+      value: 0.8617499339148824
+    - name: Accuracy
+      type: accuracy
+      value: 0.9714166181062949
+---
+
+<!-- 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. -->
+
+# xlm-roberta-base_LeNER-Br
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the lener_br dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Precision: 0.8295
+- Recall: 0.8966
+- F1: 0.8617
+- Accuracy: 0.9714
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2394        | 1.0   | 979  | nan             | 0.7134    | 0.8614 | 0.7805 | 0.9638   |
+| 0.0375        | 2.0   | 1958 | nan             | 0.8035    | 0.9043 | 0.8509 | 0.9670   |
+| 0.0256        | 3.0   | 2937 | nan             | 0.8026    | 0.8878 | 0.8430 | 0.9761   |
+| 0.0194        | 4.0   | 3916 | nan             | 0.7836    | 0.8861 | 0.8317 | 0.9670   |
+| 0.015         | 5.0   | 4895 | nan             | 0.8061    | 0.8988 | 0.8499 | 0.9691   |
+| 0.0098        | 6.0   | 5874 | nan             | 0.8279    | 0.9076 | 0.8659 | 0.9715   |
+| 0.0082        | 7.0   | 6853 | nan             | 0.8067    | 0.8905 | 0.8465 | 0.9681   |
+| 0.0042        | 8.0   | 7832 | nan             | 0.8233    | 0.9021 | 0.8609 | 0.9737   |
+| 0.0037        | 9.0   | 8811 | nan             | 0.8281    | 0.9010 | 0.8630 | 0.9712   |
+| 0.0031        | 10.0  | 9790 | nan             | 0.8295    | 0.8966 | 0.8617 | 0.9714   |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1