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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ff6cae9ae256629ee2c35043076bb30189f9ae8552261767c0211aa5136454a
- Size of remote file:
- 1.47 GB
- SHA256:
- b6d879f0b6908423a11beb32d2047d0d082583118245ccd87e83de309a47bb2e
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