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 scheduler.pt from aequa-tech/sentiment-it: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/aequa-tech/sentiment-it/resolve/063e01612d21eb0d4de98a7c9d15d905904e34b9/scheduler.pt
- Command line
-
hf download hf://aequa-tech/sentiment-it@063e01612d21eb0d4de98a7c9d15d905904e34b9/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/aequa-tech/sentiment-it/resolve/063e01612d21eb0d4de98a7c9d15d905904e34b9/scheduler.pt
1.06 kB
- Xet hash:
- 1a433618e0f3c0f79dec982d283c2d827466444f674b1c281da2c307693828d0
- Size of remote file:
- 1.06 kB
- SHA256:
- 294a2cbc9f8d1f1da5a37e31d9fe1a8765e61888c57d0937b3de64f71d5bbbc2
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