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")# 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
- Xet hash:
- cd6bd1c91f621967700baea89ee135902dfc75e2b7248f47003ce276ca77aa3f
- Size of remote file:
- 950 MB
- SHA256:
- 9ae74bfde3375fb8ee77ff63ad457ed7fed9a9f187529b480d88e2ca2b44f5bf
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