Text Classification
Transformers
PyTorch
Safetensors
Marathi
English
multilingual
bert
codemix
text-embeddings-inference
Instructions to use l3cube-pune/me-hate-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/me-hate-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/me-hate-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/me-hate-bert") model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/me-hate-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from l3cube-pune/me-hate-bert: direct link, hf CLI and curl.
- Browser
- Download file 6.41 MB
-
https://huggingface.co/l3cube-pune/me-hate-bert/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://l3cube-pune/me-hate-bert@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/l3cube-pune/me-hate-bert/resolve/refs%2Fpr%2F1/tokenizer.json
6.41 MB
File too large to display, you can check the raw version instead.