Text Classification
Transformers
Safetensors
English
bert
sentiment-analysis
finance
english
text-embeddings-inference
Instructions to use marcev/financebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcev/financebert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcev/financebert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marcev/financebert") model = AutoModelForSequenceClassification.from_pretrained("marcev/financebert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 093e8881e5479dbb821ab54db2bda24708da9d4f043e716af6f15802c070cbb9
- Size of remote file:
- 438 MB
- SHA256:
- 3be657f243263977d74f460f8d11ffa5552ce37b7a104ecf3f2567904a22b45a
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