Instructions to use abhilash1910/financial_roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abhilash1910/financial_roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="abhilash1910/financial_roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("abhilash1910/financial_roberta") model = AutoModelForMaskedLM.from_pretrained("abhilash1910/financial_roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from abhilash1910/financial_roberta: direct link, hf CLI and curl.
- Browser
- Download file 346 MB
-
https://huggingface.co/abhilash1910/financial_roberta/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://abhilash1910/financial_roberta/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/abhilash1910/financial_roberta/resolve/main/flax_model.msgpack
346 MB
- Xet hash:
- 6a0d16de10412ea8fd43e4003824ade8aa70161689070db820a095fe916e1091
- Size of remote file:
- 346 MB
- SHA256:
- a9fd170d366e63640b5320d74ba10d8d0371c3d5461bf5dfa7f1877ffd44306b
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