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--- |
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license: apache-2.0 |
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base_model: distilbert/distilbert-base-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: distilbert-base-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: 0.7845931433292028 |
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- name: Recall |
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type: recall |
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value: 0.7810444078947368 |
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- name: F1 |
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type: f1 |
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value: 0.7828147537605605 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9671762427683093 |
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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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# distilbert-base-uncased-finetuned-ner-lenerBr |
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the lener_br dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1555 |
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- Precision: 0.7846 |
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- Recall: 0.7810 |
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- F1: 0.7828 |
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- Accuracy: 0.9672 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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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 | 1.0 | 490 | 0.1861 | 0.6380 | 0.6661 | 0.6518 | 0.9446 | |
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| 0.2629 | 2.0 | 980 | 0.1618 | 0.7063 | 0.7303 | 0.7181 | 0.9537 | |
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| 0.0756 | 3.0 | 1470 | 0.1299 | 0.7299 | 0.8010 | 0.7638 | 0.9645 | |
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| 0.0443 | 4.0 | 1960 | 0.1422 | 0.7634 | 0.7708 | 0.7671 | 0.9643 | |
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| 0.0279 | 5.0 | 2450 | 0.1508 | 0.7870 | 0.7679 | 0.7773 | 0.9648 | |
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| 0.0203 | 6.0 | 2940 | 0.1457 | 0.7693 | 0.7815 | 0.7753 | 0.9681 | |
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| 0.0143 | 7.0 | 3430 | 0.1508 | 0.7767 | 0.7714 | 0.7740 | 0.9663 | |
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| 0.0105 | 8.0 | 3920 | 0.1537 | 0.7812 | 0.7669 | 0.7739 | 0.9671 | |
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| 0.0085 | 9.0 | 4410 | 0.1564 | 0.7809 | 0.7681 | 0.7745 | 0.9669 | |
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| 0.0064 | 10.0 | 4900 | 0.1555 | 0.7846 | 0.7810 | 0.7828 | 0.9672 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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