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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0070652b69686d984ef673f1d9b6650d640e200f67bcaa58ee02cb997026d41
- Size of remote file:
- 2.86 kB
- SHA256:
- 0472f6463f4dfa6cf999584b9d6fd53d1a5eb8f38caaaef68f2961121dd5e971
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