# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("jon-t/Bio_ClinicalBERT_QA")
model = AutoModelForQuestionAnswering.from_pretrained("jon-t/Bio_ClinicalBERT_QA")Quick Links
Bio_ClinicalBERT_QA
This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the Eladio/emrqa-msquad and the squad_v2 datasets.
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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2.0
Training results
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu118
- Datasets 3.3.2
- Tokenizers 0.21.0
- Downloads last month
- 5
Model tree for jon-t/Bio_ClinicalBERT_QA
Base model
emilyalsentzer/Bio_ClinicalBERT
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="jon-t/Bio_ClinicalBERT_QA")