Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Granoladata/contrast_classifier_bio_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Granoladata/contrast_classifier_bio_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Granoladata/contrast_classifier_bio_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Granoladata/contrast_classifier_bio_bert") model = AutoModelForSequenceClassification.from_pretrained("Granoladata/contrast_classifier_bio_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a8f76a65d69993cbdde4d1981e373ae7c9ac10090206b5db0d2f66a6fa7137b
- Size of remote file:
- 4.6 kB
- SHA256:
- 5e0f81e1547e54f196c9e8f4bd4dd633f1dbfb7f1a697e2966d96fdc184bb4d6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.