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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("abhilash1910/financial_roberta") model = AutoModelForMaskedLM.from_pretrained("abhilash1910/financial_roberta") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 171757e52b7bbfcb01792e96f84ee1f0e9089c6a14e9bdbd1edcb5b18dedbbd8
- Size of remote file:
- 346 MB
- SHA256:
- 800c03f97b1c0eb68d06bea6c18586f3b343764e386a5689de7ddee7a349d9af
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