Text Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use dipteshkanojia/hing-roberta-CM-run-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dipteshkanojia/hing-roberta-CM-run-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dipteshkanojia/hing-roberta-CM-run-3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dipteshkanojia/hing-roberta-CM-run-3") model = AutoModelForSequenceClassification.from_pretrained("dipteshkanojia/hing-roberta-CM-run-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cef4b9be8c033feb4b6d8d5edcddbea58b66f48345e165032b5c160e1e22d7eb
- Size of remote file:
- 3.25 kB
- SHA256:
- e10432b80d194310e797cf15acf9a822d8b598c53c8a135b90ed3bc639a4fc48
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