Text Classification
Transformers
PyTorch
Safetensors
Marathi
English
multilingual
xlm-roberta
codemix
text-embeddings-inference
Instructions to use l3cube-pune/me-hate-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/me-hate-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/me-hate-roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/me-hate-roberta") model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/me-hate-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from l3cube-pune/me-hate-roberta: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/l3cube-pune/me-hate-roberta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://l3cube-pune/me-hate-roberta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/l3cube-pune/me-hate-roberta/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 361b593984314024b8af4b8c438c38ce1f023ed20379f01d644078532e6e6a75
- Size of remote file:
- 1.11 GB
- SHA256:
- 545421b1383137f97c00a9c3f15c73ae6a8dfc7240d53f02640c65712f7603c7
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