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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google-bert/bert-large-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- lener_br |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: bert-large-uncased-finetuned-ner-lenerBr |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: lener_br |
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type: lener_br |
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config: lener_br |
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split: validation |
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args: lener_br |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8195459032576505 |
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- name: Recall |
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type: recall |
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value: 0.8534128289473685 |
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- name: F1 |
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type: f1 |
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value: 0.8361365696444758 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9658050781203017 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-large-uncased-finetuned-ner-lenerBr |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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- Precision: 0.8195 |
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- Recall: 0.8534 |
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- F1: 0.8361 |
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- Accuracy: 0.9658 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 16 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 0.9995 | 489 | nan | 0.6811 | 0.7451 | 0.7116 | 0.9503 | |
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| 0.1982 | 1.9990 | 978 | nan | 0.7258 | 0.8314 | 0.7750 | 0.9536 | |
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| 0.0517 | 2.9985 | 1467 | nan | 0.7487 | 0.8238 | 0.7845 | 0.9587 | |
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| 0.0289 | 4.0 | 1957 | nan | 0.7801 | 0.8684 | 0.8219 | 0.9641 | |
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| 0.0191 | 4.9995 | 2446 | nan | 0.7986 | 0.8567 | 0.8266 | 0.9665 | |
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| 0.0138 | 5.9990 | 2935 | nan | 0.8120 | 0.8491 | 0.8302 | 0.9642 | |
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| 0.0097 | 6.9985 | 3424 | nan | 0.8201 | 0.8643 | 0.8416 | 0.9663 | |
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| 0.0076 | 8.0 | 3914 | nan | 0.8079 | 0.8672 | 0.8365 | 0.9660 | |
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| 0.0053 | 8.9995 | 4403 | nan | 0.8211 | 0.8409 | 0.8309 | 0.9662 | |
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| 0.0041 | 9.9949 | 4890 | nan | 0.8195 | 0.8534 | 0.8361 | 0.9658 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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