Text Classification
Transformers
PyTorch
Safetensors
Marathi
English
multilingual
roberta
codemix
text-embeddings-inference
Instructions to use l3cube-pune/me-sent-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/me-sent-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/me-sent-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/me-sent-roberta") model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/me-sent-roberta") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c8dbd752c41e425b53e1c18e45c91eeef0353025bdc97de47e8023577ec71db
- Size of remote file:
- 1.11 GB
- SHA256:
- 430b50b0c9fd76da2a80ae3d2ef3fa411c23de4123617d1f648d4f556111c77e
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