End of training

README.md

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+---
+license: apache-2.0
+base_model: google-bert/bert-large-cased
+tags:
+- generated_from_trainer
+datasets:
+- lener_br
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-large-cased-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: 0.8045846203869289
+    - name: Recall
+      type: recall
+      value: 0.82981220657277
+    - name: F1
+      type: f1
+      value: 0.8170037144036318
+    - name: Accuracy
+      type: accuracy
+      value: 0.9644917654463443
+---
+
+<!-- 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. -->
+
+# bert-large-cased-finetuned-ner-lenerBr
+
+This model is a fine-tuned version of [google-bert/bert-large-cased](https://huggingface.co/google-bert/bert-large-cased) on the lener_br dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Precision: 0.8046
+- Recall: 0.8298
+- F1: 0.8170
+- 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: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 32
+- 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        | 0.9974 | 244  | nan             | 0.6627    | 0.7490 | 0.7032 | 0.9402   |
+| No log        | 1.9990 | 489  | nan             | 0.7002    | 0.8005 | 0.7470 | 0.9503   |
+| 0.1592        | 2.9964 | 733  | nan             | 0.7482    | 0.8080 | 0.7769 | 0.9545   |
+| 0.1592        | 3.9980 | 978  | nan             | 0.7749    | 0.8166 | 0.7952 | 0.9614   |
+| 0.0279        | 4.9995 | 1223 | nan             | 0.7845    | 0.7973 | 0.7909 | 0.9634   |
+| 0.0279        | 5.9969 | 1467 | nan             | 0.7840    | 0.8203 | 0.8017 | 0.9622   |
+| 0.0122        | 6.9985 | 1712 | nan             | 0.7989    | 0.8224 | 0.8105 | 0.9638   |
+| 0.0122        | 8.0    | 1957 | nan             | 0.7977    | 0.8286 | 0.8129 | 0.9634   |
+| 0.007         | 8.9974 | 2201 | nan             | 0.7947    | 0.8265 | 0.8103 | 0.9643   |
+| 0.007         | 9.9745 | 2440 | nan             | 0.8046    | 0.8298 | 0.8170 | 0.9645   |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.1.2
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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