End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert/distilbert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- lener_br
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: distilbert-base-uncased-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: 0.7477750426055672
+    - name: Recall
+      type: recall
+      value: 0.8118832236842105
+    - name: F1
+      type: f1
+      value: 0.7785115820601283
+    - name: Accuracy
+      type: accuracy
+      value: 0.9644699967525048
+---
+
+<!-- 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. -->
+
+# distilbert-base-uncased-finetuned-ner-lenerBr
+
+This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the lener_br dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1546
+- Precision: 0.7478
+- Recall: 0.8119
+- F1: 0.7785
+- Accuracy: 0.9645
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 100
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 490  | 0.2131          | 0.6201    | 0.6604 | 0.6396 | 0.9359   |
+| 0.264         | 2.0   | 980  | 0.1828          | 0.7004    | 0.7504 | 0.7246 | 0.9508   |
+| 0.0776        | 3.0   | 1470 | 0.1564          | 0.6582    | 0.8137 | 0.7278 | 0.9537   |
+| 0.0437        | 4.0   | 1960 | 0.1644          | 0.7485    | 0.7623 | 0.7553 | 0.9573   |
+| 0.0288        | 5.0   | 2450 | 0.1555          | 0.7620    | 0.7662 | 0.7641 | 0.9614   |
+| 0.0208        | 6.0   | 2940 | 0.1874          | 0.7530    | 0.7759 | 0.7643 | 0.9550   |
+| 0.0143        | 7.0   | 3430 | 0.1546          | 0.7478    | 0.8119 | 0.7785 | 0.9645   |
+| 0.0117        | 8.0   | 3920 | 0.1717          | 0.7014    | 0.7677 | 0.7330 | 0.9592   |
+| 0.0102        | 9.0   | 4410 | 0.1884          | 0.7734    | 0.7714 | 0.7724 | 0.9613   |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.1.2
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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