Instructions to use aequa-tech/sentiment-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aequa-tech/sentiment-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aequa-tech/sentiment-it")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aequa-tech/sentiment-it") model = AutoModelForSequenceClassification.from_pretrained("aequa-tech/sentiment-it") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a8f6eed62b947dbdf0c6daa943e4742bf992876373691f98f80fb0c1d08ff7b
- Size of remote file:
- 5.05 kB
- SHA256:
- 285ea59fa8936381ad7ee4ca536a1b6ce932342f81985c2503d0b2279c0b6443
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