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--- |
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license: mit |
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base_model: xlnet/xlnet-base-cased |
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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: XLNet-base_LeNER-Br |
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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: 0.8062054933875891 |
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- name: Recall |
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type: recall |
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value: 0.872317006053935 |
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- name: F1 |
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type: f1 |
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value: 0.8379592915675389 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9783680282796544 |
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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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# XLNet-base_LeNER-Br |
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This model is a fine-tuned version of [xlnet/xlnet-base-cased](https://huggingface.co./xlnet/xlnet-base-cased) 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: 0.8062 |
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- Recall: 0.8723 |
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- F1: 0.8380 |
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- Accuracy: 0.9784 |
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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: 8 |
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- eval_batch_size: 8 |
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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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| 0.2531 | 1.0 | 979 | nan | 0.6037 | 0.7788 | 0.6801 | 0.9602 | |
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| 0.0531 | 2.0 | 1958 | nan | 0.6865 | 0.8184 | 0.7467 | 0.9657 | |
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| 0.0344 | 3.0 | 2937 | nan | 0.7079 | 0.8321 | 0.7650 | 0.9697 | |
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| 0.0214 | 4.0 | 3916 | nan | 0.7739 | 0.8514 | 0.8108 | 0.9765 | |
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| 0.0176 | 5.0 | 4895 | nan | 0.7407 | 0.8520 | 0.7924 | 0.9712 | |
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| 0.0109 | 6.0 | 5874 | nan | 0.7984 | 0.8696 | 0.8325 | 0.9773 | |
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| 0.0093 | 7.0 | 6853 | nan | 0.7944 | 0.8657 | 0.8285 | 0.9778 | |
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| 0.0056 | 8.0 | 7832 | nan | 0.8130 | 0.8756 | 0.8431 | 0.9779 | |
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| 0.0041 | 9.0 | 8811 | nan | 0.8171 | 0.8751 | 0.8451 | 0.9781 | |
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| 0.0034 | 10.0 | 9790 | nan | 0.8062 | 0.8723 | 0.8380 | 0.9784 | |
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#### Testing results |
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metrics={'test_loss': 0.10678809881210327, 'test_precision': 0.8132832080200502, 'test_recall': 0.8670674682698731, 'test_f1': 0.8393145813126414, 'test_accuracy': 0.9862667593953853, 'test_runtime': 42.9969, 'test_samples_per_second': 32.328, 'test_steps_per_second': 4.047}) |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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