Instructions to use Jaafer/diseaseBertCDI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jaafer/diseaseBertCDI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Jaafer/diseaseBertCDI")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Jaafer/diseaseBertCDI") model = AutoModelForMaskedLM.from_pretrained("Jaafer/diseaseBertCDI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
diseaseBertCDI
This model is a fine-tuned version of bert-base-uncased on the None dataset.
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: 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: 40
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for Jaafer/diseaseBertCDI
Base model
google-bert/bert-base-uncased