Instructions to use sm6342/FinRoberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sm6342/FinRoberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sm6342/FinRoberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sm6342/FinRoberta") model = AutoModelForMaskedLM.from_pretrained("sm6342/FinRoberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sm6342/FinRoberta: direct link, hf CLI and curl.
- Browser
- Download file 2.35 kB
-
https://huggingface.co/sm6342/FinRoberta/resolve/main/training_args.bin
- Command line
-
hf download hf://sm6342/FinRoberta/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sm6342/FinRoberta/resolve/main/training_args.bin
2.35 kB
- Xet hash:
- c23641bcd3a10d8b2d98ccfc0eafba3e0b0542f2b116010fad4bcca9801ca3cf
- Size of remote file:
- 2.35 kB
- SHA256:
- 81beae7ff22b2fb72ae2cac99785e1036710b28b2310da3b817cab4dbf98b33a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.