Instructions to use radmada/FinBERT-BaseVocab-Cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radmada/FinBERT-BaseVocab-Cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="radmada/FinBERT-BaseVocab-Cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("radmada/FinBERT-BaseVocab-Cased") model = AutoModelForMaskedLM.from_pretrained("radmada/FinBERT-BaseVocab-Cased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 355d573aadb4d4c7c4992ba276c3d119cb3992f8b486832d8f5f8c3738c58077
- Size of remote file:
- 436 MB
- SHA256:
- 90788d1001842f6b174c26f0af7831da1674ca11a31684ec1f794b8eb5ab4a54
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