End of training

README.md

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+---
+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-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: 0.7616079105760963
+    - name: Recall
+      type: recall
+      value: 0.9157405014215559
+    - name: F1
+      type: f1
+      value: 0.8315925360873138
+    - name: Accuracy
+      type: accuracy
+      value: 0.968278871008711
+---
+
+<!-- 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-finetuned-ner-lenerBr
+
+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: 0.1525
+- Precision: 0.7616
+- Recall: 0.9157
+- F1: 0.8316
+- Accuracy: 0.9683
+
+## 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: 32
+- eval_batch_size: 32
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 245  | 0.1557          | 0.6755    | 0.7609 | 0.7157 | 0.9513   |
+| No log        | 2.0   | 490  | 0.1627          | 0.6504    | 0.8599 | 0.7407 | 0.9540   |
+| 0.2052        | 3.0   | 735  | 0.1459          | 0.7109    | 0.9036 | 0.7957 | 0.9645   |
+| 0.2052        | 4.0   | 980  | 0.1590          | 0.7607    | 0.8790 | 0.8156 | 0.9633   |
+| 0.0303        | 5.0   | 1225 | 0.1554          | 0.7188    | 0.8987 | 0.7988 | 0.9642   |
+| 0.0303        | 6.0   | 1470 | 0.1467          | 0.7333    | 0.9294 | 0.8198 | 0.9654   |
+| 0.0171        | 7.0   | 1715 | 0.1356          | 0.7665    | 0.8995 | 0.8277 | 0.9683   |
+| 0.0171        | 8.0   | 1960 | 0.1475          | 0.8106    | 0.8935 | 0.8500 | 0.9672   |
+| 0.0104        | 9.0   | 2205 | 0.1441          | 0.7607    | 0.9196 | 0.8327 | 0.9690   |
+| 0.0104        | 10.0  | 2450 | 0.1525          | 0.7616    | 0.9157 | 0.8316 | 0.9683   |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.1.2
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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