Instructions to use priyabrat/new5th_bert_article_categorisation_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use priyabrat/new5th_bert_article_categorisation_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="priyabrat/new5th_bert_article_categorisation_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("priyabrat/new5th_bert_article_categorisation_model") model = AutoModelForSequenceClassification.from_pretrained("priyabrat/new5th_bert_article_categorisation_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a0537321ee95677594d493a0969c03d2ae17b5e349b20c1919fd42b0f51cffc
- Size of remote file:
- 438 MB
- SHA256:
- 0cb4fef2bb49282474bb997b77d4d2fb8730b205595fc383e71eb26589bbdfcd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.