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")# pip install -U transformers accelerate # 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 optimizer.pt from aequa-tech/sentiment-it: direct link, hf CLI and curl.
- Browser
- Download file 1.47 GB
-
https://huggingface.co/aequa-tech/sentiment-it/resolve/063e01612d21eb0d4de98a7c9d15d905904e34b9/optimizer.pt
- Command line
-
hf download hf://aequa-tech/sentiment-it@063e01612d21eb0d4de98a7c9d15d905904e34b9/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/aequa-tech/sentiment-it/resolve/063e01612d21eb0d4de98a7c9d15d905904e34b9/optimizer.pt
1.47 GB
- Xet hash:
- 4ff6cae9ae256629ee2c35043076bb30189f9ae8552261767c0211aa5136454a
- Size of remote file:
- 1.47 GB
- SHA256:
- b6d879f0b6908423a11beb32d2047d0d082583118245ccd87e83de309a47bb2e
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