Instructions to use Tomohiro/MediA_C with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tomohiro/MediA_C with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tomohiro/MediA_C")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tomohiro/MediA_C") model = AutoModelForSequenceClassification.from_pretrained("Tomohiro/MediA_C") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c17ccd4d847dc31c4a6700357c4bc3cfe991993f158774f6816cac0d057b8c6f
- Size of remote file:
- 443 MB
- SHA256:
- 129106a701cfd7e4f1f219b2ee04bbdefc8bcf356455b2ef7bcec95ba4d7ab4f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.