Instructions to use Kumshe/Hausa-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kumshe/Hausa-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kumshe/Hausa-sentiment-analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kumshe/Hausa-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("Kumshe/Hausa-sentiment-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Kumshe/Hausa-sentiment-analysis: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/Kumshe/Hausa-sentiment-analysis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Kumshe/Hausa-sentiment-analysis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Kumshe/Hausa-sentiment-analysis/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 315af2a384a0af3f1981c697c6d814c728a4ffefdbf1fe0d4c32625412beb902
- Size of remote file:
- 433 MB
- SHA256:
- 1f15192d4bfd8be684803ff56de38f77f8e40994691380d56252d46419acfb7c
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