Instructions to use tim9510019/Financial-Sentiment-Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tim9510019/Financial-Sentiment-Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tim9510019/Financial-Sentiment-Analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tim9510019/Financial-Sentiment-Analysis") model = AutoModelForSequenceClassification.from_pretrained("tim9510019/Financial-Sentiment-Analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- acc205f50f4b39291a927c875b01236d6d5763548e6dfc2d1007a8cdb3aa606d
- Size of remote file:
- 439 MB
- SHA256:
- 30e7379fa40f29fd894d9515442c3d24ea56e3adb0b83fd68aaa7bdf9fe5fa58
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