Instructions to use snowdere/small_sentiment_fin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use snowdere/small_sentiment_fin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="snowdere/small_sentiment_fin")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("snowdere/small_sentiment_fin") model = AutoModelForSequenceClassification.from_pretrained("snowdere/small_sentiment_fin") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3ac74e9d4c5c89e9c7b772a64781377227404645d488c4a936a7d1e1492a874
- Size of remote file:
- 90.9 MB
- SHA256:
- 75a2d695fb76de6d3f9cb1b8adf2cc1c92d95a66864fe0cac9725d6a9a953c09
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