Instructions to use vr25/fin_BERT-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vr25/fin_BERT-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vr25/fin_BERT-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vr25/fin_BERT-v1") model = AutoModelForMaskedLM.from_pretrained("vr25/fin_BERT-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9147fd8a29289bd60b66984e363e3206f69080f4d66e78836201cac90c96e73
- Size of remote file:
- 266 MB
- SHA256:
- d5f9be7404b05e4636b85861d015e71d7f58f64fd81ce29f14ae9f6e44f346a0
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