Text Classification
Transformers
PyTorch
English
roberta
fill-mask
finance
text-embeddings-inference
Instructions to use SUFEHeisenberg/Fin-RoBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SUFEHeisenberg/Fin-RoBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SUFEHeisenberg/Fin-RoBERTa")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SUFEHeisenberg/Fin-RoBERTa") model = AutoModelForMaskedLM.from_pretrained("SUFEHeisenberg/Fin-RoBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 366a960f0674c95d4fecb3091d10c5b67278df036bf408aeeec170cb7f4c86a2
- Size of remote file:
- 505 MB
- SHA256:
- b7ba308348c49435168e004595ff4b0713c1911e5ba952d428d9b0351a625973
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