# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("adalbertojunior/test-medner")
model = AutoModelForTokenClassification.from_pretrained("adalbertojunior/test-medner")Quick Links
test-ner-4
This model is a fine-tuned version of pucpr/biobertpt-all on the adalbertojunior/substance_quantity dataset. It achieves the following results on the evaluation set:
- Loss: 0.0001
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.11.0
- Tokenizers 0.14.1
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Model tree for adalbertojunior/test-medner
Base model
pucpr/biobertpt-allDataset used to train adalbertojunior/test-medner
Evaluation results
- Precision on adalbertojunior/substance_quantityself-reported1.000
- Recall on adalbertojunior/substance_quantityself-reported1.000
- F1 on adalbertojunior/substance_quantityself-reported1.000
- Accuracy on adalbertojunior/substance_quantityself-reported1.000
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="adalbertojunior/test-medner")