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
Download pytorch_model.bin from aequa-tech/sentiment-it: direct link, hf CLI and curl.
- Browser
- Download file 737 MB
-
https://huggingface.co/aequa-tech/sentiment-it/resolve/871b91f02ededfab55740903434f84e2f184c180/pytorch_model.bin
- Command line
-
hf download hf://aequa-tech/sentiment-it@871b91f02ededfab55740903434f84e2f184c180/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aequa-tech/sentiment-it/resolve/871b91f02ededfab55740903434f84e2f184c180/pytorch_model.bin
737 MB
- Xet hash:
- 3d99618d05f26f10ddc7441d5c61996663e59b20f93d834b403bb7c76a416063
- Size of remote file:
- 737 MB
- SHA256:
- 2143fe3b7e1951f0c04f804f467f28c71e22475e8fd01f64a1030ac34719d696
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