Text Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use dipteshkanojia/hing-roberta-CM-run-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dipteshkanojia/hing-roberta-CM-run-4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dipteshkanojia/hing-roberta-CM-run-4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dipteshkanojia/hing-roberta-CM-run-4") model = AutoModelForSequenceClassification.from_pretrained("dipteshkanojia/hing-roberta-CM-run-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dipteshkanojia/hing-roberta-CM-run-4: direct link, hf CLI and curl.
- Browser
- Download file 3.25 kB
-
https://huggingface.co/dipteshkanojia/hing-roberta-CM-run-4/resolve/main/training_args.bin
- Command line
-
hf download hf://dipteshkanojia/hing-roberta-CM-run-4/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dipteshkanojia/hing-roberta-CM-run-4/resolve/main/training_args.bin
3.25 kB
- Xet hash:
- d63da750dd50127e1d5585b3138844d75ad0d7aba580c6e9ec5e116c173fd44c
- Size of remote file:
- 3.25 kB
- SHA256:
- d5b0f841c176e8da4120bcf1212a8829fcf3893aa803a5e63501692f45ebbe72
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