Instructions to use SNV/distilbert-stock-tweet-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SNV/distilbert-stock-tweet-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SNV/distilbert-stock-tweet-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SNV/distilbert-stock-tweet-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("SNV/distilbert-stock-tweet-sentiment-analysis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d37ac43e748aa4f2a081dd8bfca748b56cd2038d5348ee3503342487b7f9e79d
- Size of remote file:
- 268 MB
- SHA256:
- edbdf13d43737960341f7951f8a9265aa25beb48afd4abe5f4494645e25ce44c
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