Instructions to use NLP-EXP/QST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NLP-EXP/QST with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NLP-EXP/QST")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NLP-EXP/QST") model = AutoModelForMaskedLM.from_pretrained("NLP-EXP/QST", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from NLP-EXP/QST: direct link, hf CLI and curl.
- Browser
- Download file 224 Bytes
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https://huggingface.co/NLP-EXP/QST/resolve/main/README.md
- Command line
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hf download hf://NLP-EXP/QST/README.md
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curl -L -o README.md https://huggingface.co/NLP-EXP/QST/resolve/main/README.md
224 Bytes
metadata
language:
- ar
the QARiB-Sentiment-Tweets (QST) Model is a QARiB language model that was further pre-trained for 300K training steps and roughly 28 epochs using sentiment tweets dataset (contains over 682,000 tweets)