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:
- 302eee1443b2b06b3578f673f02435b0fbe59563066967cfbac9e69d29423d2a
- Size of remote file:
- 1.11 GB
- SHA256:
- b5824c20574dc174e095743399e2929c14ab8978595e4b0ae9f8d7db873fa23a
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