Instructions to use ahmedrachid/FinancialBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedrachid/FinancialBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ahmedrachid/FinancialBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ahmedrachid/FinancialBERT") model = AutoModelForMaskedLM.from_pretrained("ahmedrachid/FinancialBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3be9157a1d3e92150a083916e505c8fb4133da62261d606439d494324ace4ee4
- Size of remote file:
- 439 MB
- SHA256:
- 0ca8d153f1176ab4c32c84920399708f9b141829f6634b8121cdc3a8a59dd690
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