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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: 0.7909 |
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- Accuracy: 0.756 |
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- Precision: 0.5226 |
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- Recall: 0.3518 |
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- F1: 0.4027 |
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- Weighted F1: 0.7173 |
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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 | 13 | 0.9045 | 0.7251 | 0.3378 | 0.1564 | 0.1457 | 0.6233 | |
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| No log | 2.0 | 26 | 0.8688 | 0.7336 | 0.5559 | 0.2281 | 0.2504 | 0.6453 | |
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| No log | 3.0 | 39 | 0.8579 | 0.7409 | 0.5851 | 0.2931 | 0.3179 | 0.6795 | |
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| No log | 4.0 | 52 | 0.7956 | 0.7507 | 0.5225 | 0.3443 | 0.3919 | 0.7017 | |
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| No log | 5.0 | 65 | 0.7947 | 0.7529 | 0.532 | 0.3535 | 0.4026 | 0.7093 | |
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| No log | 6.0 | 78 | 0.8063 | 0.7549 | 0.5502 | 0.3752 | 0.4191 | 0.7168 | |
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| No log | 7.0 | 91 | 0.8059 | 0.7599 | 0.5496 | 0.3764 | 0.4269 | 0.7227 | |
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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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