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--- |
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language: |
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- en |
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license: mit |
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base_model: xlnet-base-cased |
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tags: |
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- low-resource NER |
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- token_classification |
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- biomedicine |
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- medical NER |
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- generated_from_trainer |
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datasets: |
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- medicine |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: Dagobert42/xlnet-base-cased-biored-finetuned |
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results: [] |
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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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# Dagobert42/xlnet-base-cased-biored-finetuned |
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This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the bigbio/biored dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7341 |
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- Accuracy: 0.7714 |
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- Precision: 0.5341 |
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- Recall: 0.4169 |
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- F1: 0.4594 |
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- Weighted F1: 0.7495 |
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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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:| |
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| No log | 1.0 | 25 | 0.8831 | 0.7377 | 0.4536 | 0.2481 | 0.2822 | 0.6719 | |
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| No log | 2.0 | 50 | 0.8309 | 0.7542 | 0.6035 | 0.3177 | 0.3598 | 0.6933 | |
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| No log | 3.0 | 75 | 0.7695 | 0.7624 | 0.568 | 0.3566 | 0.409 | 0.7189 | |
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| No log | 4.0 | 100 | 0.7562 | 0.7676 | 0.5536 | 0.3886 | 0.4398 | 0.7343 | |
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| No log | 5.0 | 125 | 0.7540 | 0.7673 | 0.5711 | 0.4013 | 0.4474 | 0.7368 | |
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| No log | 6.0 | 150 | 0.7425 | 0.7754 | 0.5867 | 0.4398 | 0.4873 | 0.7514 | |
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| No log | 7.0 | 175 | 0.7806 | 0.7788 | 0.606 | 0.4235 | 0.473 | 0.7475 | |
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| No log | 8.0 | 200 | 0.7638 | 0.7785 | 0.558 | 0.4547 | 0.4871 | 0.7549 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.15.0 |
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