End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: FacebookAI/xlm-roberta-large
+tags:
+- generated_from_trainer
+datasets:
+- lener_br
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: xlm-roberta-large-finetuned-ner-lenerBr
+  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: 0.9166029074215761
+    - name: Recall
+      type: recall
+      value: 0.9289222021194107
+    - name: F1
+      type: f1
+      value: 0.9227214377406933
+    - name: Accuracy
+      type: accuracy
+      value: 0.9853721218641206
+---
+
+<!-- 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-large-finetuned-ner-lenerBr
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the lener_br dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Precision: 0.9166
+- Recall: 0.9289
+- F1: 0.9227
+- Accuracy: 0.9854
+
+## 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: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 16
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 0.9995 | 489  | nan             | 0.8191    | 0.8167 | 0.8179 | 0.9751   |
+| 0.163         | 1.9990 | 978  | nan             | 0.8600    | 0.9080 | 0.8833 | 0.9790   |
+| 0.0427        | 2.9985 | 1467 | nan             | 0.8736    | 0.9163 | 0.8944 | 0.9814   |
+| 0.0279        | 4.0    | 1957 | nan             | 0.8688    | 0.9191 | 0.8932 | 0.9801   |
+| 0.019         | 4.9995 | 2446 | nan             | 0.9123    | 0.9196 | 0.9159 | 0.9840   |
+| 0.0143        | 5.9990 | 2935 | nan             | 0.9008    | 0.9346 | 0.9174 | 0.9842   |
+| 0.0112        | 6.9985 | 3424 | nan             | 0.9063    | 0.9250 | 0.9156 | 0.9843   |
+| 0.0072        | 8.0    | 3914 | nan             | 0.8954    | 0.9315 | 0.9131 | 0.9841   |
+| 0.0065        | 8.9995 | 4403 | nan             | 0.9226    | 0.9245 | 0.9236 | 0.9857   |
+| 0.0048        | 9.9949 | 4890 | nan             | 0.9166    | 0.9289 | 0.9227 | 0.9854   |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

runs/Dec03_13-44-27_316eb741e5c2/events.out.tfevents.1733233481.316eb741e5c2.554.0

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