Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use okho0653/Bio_ClinicalBERT-zero-shot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use okho0653/Bio_ClinicalBERT-zero-shot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="okho0653/Bio_ClinicalBERT-zero-shot")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("okho0653/Bio_ClinicalBERT-zero-shot") model = AutoModelForSequenceClassification.from_pretrained("okho0653/Bio_ClinicalBERT-zero-shot") - Notebooks
- Google Colab
- Kaggle
Bio_ClinicalBERT-zero-shot
This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.5417
- eval_accuracy: 1.0
- eval_f1: 1.0
- eval_runtime: 4.3261
- eval_samples_per_second: 6.241
- eval_steps_per_second: 0.462
- step: 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
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