Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use jayavibhav/roberta-classification-10ksamples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayavibhav/roberta-classification-10ksamples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jayavibhav/roberta-classification-10ksamples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jayavibhav/roberta-classification-10ksamples") model = AutoModelForSequenceClassification.from_pretrained("jayavibhav/roberta-classification-10ksamples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jayavibhav/roberta-classification-10ksamples: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/jayavibhav/roberta-classification-10ksamples/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jayavibhav/roberta-classification-10ksamples/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jayavibhav/roberta-classification-10ksamples/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 18c56325305a4274a89443e89ac164a81e4de1ebf0119a5fc3c9dfa44e673c76
- Size of remote file:
- 499 MB
- SHA256:
- 7e833d92433ccd32d48d50356f585a702e368d26b5e22aaacc85069a3d091ebe
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