End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: google-bert/bert-large-uncased
+tags:
+- generated_from_trainer
+datasets:
+- lener_br
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-large-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: 0.8195459032576505
+    - name: Recall
+      type: recall
+      value: 0.8534128289473685
+    - name: F1
+      type: f1
+      value: 0.8361365696444758
+    - name: Accuracy
+      type: accuracy
+      value: 0.9658050781203017
+---
+
+<!-- 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-uncased-finetuned-ner-lenerBr
+
+This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on the lener_br dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Precision: 0.8195
+- Recall: 0.8534
+- F1: 0.8361
+- Accuracy: 0.9658
+
+## 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             | 0.6811    | 0.7451 | 0.7116 | 0.9503   |
+| 0.1982        | 1.9990 | 978  | nan             | 0.7258    | 0.8314 | 0.7750 | 0.9536   |
+| 0.0517        | 2.9985 | 1467 | nan             | 0.7487    | 0.8238 | 0.7845 | 0.9587   |
+| 0.0289        | 4.0    | 1957 | nan             | 0.7801    | 0.8684 | 0.8219 | 0.9641   |
+| 0.0191        | 4.9995 | 2446 | nan             | 0.7986    | 0.8567 | 0.8266 | 0.9665   |
+| 0.0138        | 5.9990 | 2935 | nan             | 0.8120    | 0.8491 | 0.8302 | 0.9642   |
+| 0.0097        | 6.9985 | 3424 | nan             | 0.8201    | 0.8643 | 0.8416 | 0.9663   |
+| 0.0076        | 8.0    | 3914 | nan             | 0.8079    | 0.8672 | 0.8365 | 0.9660   |
+| 0.0053        | 8.9995 | 4403 | nan             | 0.8211    | 0.8409 | 0.8309 | 0.9662   |
+| 0.0041        | 9.9949 | 4890 | nan             | 0.8195    | 0.8534 | 0.8361 | 0.9658   |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

runs/Dec04_13-26-17_9f879241f0da/events.out.tfevents.1733318793.9f879241f0da.450.0

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