Instructions to use ychu612/ClinicalBERT_rsavav_fn_adult2_hq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ychu612/ClinicalBERT_rsavav_fn_adult2_hq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ychu612/ClinicalBERT_rsavav_fn_adult2_hq")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ychu612/ClinicalBERT_rsavav_fn_adult2_hq") model = AutoModelForSequenceClassification.from_pretrained("ychu612/ClinicalBERT_rsavav_fn_adult2_hq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ClinicalBERT_rsavav_fn_adult2_hq
This model is a fine-tuned version of medicalai/ClinicalBERT on an unknown 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: 4e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
Framework versions
- Transformers 4.45.2
- Pytorch 2.2.1
- Datasets 3.0.1
- Tokenizers 0.20.0
- Downloads last month
- 7
Model tree for ychu612/ClinicalBERT_rsavav_fn_adult2_hq
Base model
medicalai/ClinicalBERT