Instructions to use Jingya/finbert-tone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jingya/finbert-tone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jingya/finbert-tone")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jingya/finbert-tone") model = AutoModelForSequenceClassification.from_pretrained("Jingya/finbert-tone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ced21519f19093a28e23314dcc441d790424eca44b48e75e8f1711ee2e2663e
- Size of remote file:
- 408 MB
- SHA256:
- 9519a60c86bcd38b2859b2e3bfc01ee48edadb9f5933e4f4eeea19eb05cfc4d9
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