Text Classification
Transformers
PyTorch
TensorFlow
English
bert
financial-sentiment-analysis
sentiment-analysis
Instructions to use ldeb/solved-finbert-tone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ldeb/solved-finbert-tone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ldeb/solved-finbert-tone")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ldeb/solved-finbert-tone") model = AutoModelForSequenceClassification.from_pretrained("ldeb/solved-finbert-tone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28e21fbd63982f3c26a5ece6ad938c8d25704245f46895b64bc04e9803096da6
- Size of remote file:
- 439 MB
- SHA256:
- f31c2036e91c9854bcc35141d16669dd07b9726adfe391d1011bff1de7ea4b32
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