Instructions to use ExponentialScience/LedgerBERT-Market-Sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ExponentialScience/LedgerBERT-Market-Sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ExponentialScience/LedgerBERT-Market-Sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ExponentialScience/LedgerBERT-Market-Sentiment") model = AutoModelForSequenceClassification.from_pretrained("ExponentialScience/LedgerBERT-Market-Sentiment") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24af9c03fe67057d3666a61850587d3f9f6efcc6bbe49b40097775520fcbb01d
- Size of remote file:
- 440 MB
- SHA256:
- cbf8aab28dd451c7ac63d6425b9d450858c335469ce454bc75fb5273d4777026
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