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:
- 293b0559fd309327b849b3310b3e3f1008a60ff177f5b8cfc9a4885f06e68b8e
- Size of remote file:
- 505 MB
- SHA256:
- 32c83874a4fad2a5135a5dd386c98a7f71c156000f46c5d11033cc71566a0bdc
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