Instructions to use SushantGautam/roberta-large_accuracy-coverage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SushantGautam/roberta-large_accuracy-coverage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SushantGautam/roberta-large_accuracy-coverage")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SushantGautam/roberta-large_accuracy-coverage") model = AutoModelForSequenceClassification.from_pretrained("SushantGautam/roberta-large_accuracy-coverage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from SushantGautam/roberta-large_accuracy-coverage: direct link, hf CLI and curl.
- Browser
- Download file 2.84 GB
-
https://huggingface.co/SushantGautam/roberta-large_accuracy-coverage/resolve/main/optimizer.pt
- Command line
-
hf download hf://SushantGautam/roberta-large_accuracy-coverage/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/SushantGautam/roberta-large_accuracy-coverage/resolve/main/optimizer.pt
2.84 GB
- Xet hash:
- 814d3034bab113f875117d5361481fedcba88fd10313747bafa27d4b9ad7c032
- Size of remote file:
- 2.84 GB
- SHA256:
- 219799fafb9c53a896a39ea040efaf0c1e565b20ff5ab4ad2c32e59fa17b2f84
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