Instructions to use l3cube-pune/hate-bert-hasoc-marathi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hate-bert-hasoc-marathi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/hate-bert-hasoc-marathi")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hate-bert-hasoc-marathi") model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/hate-bert-hasoc-marathi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 811efb69b75b5ec7cdad3908c4bcdd38a1a7d671f1683962999272271308212c
- Size of remote file:
- 134 MB
- SHA256:
- 3a6204b125d77b0e6232c573c079f0fb1e294c08c718a58657c25266e873d3ab
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.