Text Classification
Transformers
Safetensors
English
Portuguese
roberta
biology
science
nlp
biomedical
filter
medical
text-embeddings-inference
Instructions to use Madras1/RobertaBioClass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Madras1/RobertaBioClass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Madras1/RobertaBioClass")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Madras1/RobertaBioClass") model = AutoModelForSequenceClassification.from_pretrained("Madras1/RobertaBioClass", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fbe1dde0122148859de304be9453c3547c7dab157d481f95bd4eff9b12c8ab29
- Size of remote file:
- 5.84 kB
- SHA256:
- 30cee9201d06b92448b4964136406dca91f20f6c6e501529bc35006ac27561db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.