Instructions to use NLP-EXP/QSRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NLP-EXP/QSRT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NLP-EXP/QSRT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NLP-EXP/QSRT") model = AutoModelForMaskedLM.from_pretrained("NLP-EXP/QSRT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from NLP-EXP/QSRT: direct link, hf CLI and curl.
- Browser
- Download file 365 Bytes
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https://huggingface.co/NLP-EXP/QSRT/resolve/main/README.md
- Command line
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hf download hf://NLP-EXP/QSRT/README.md
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curl -L -o README.md https://huggingface.co/NLP-EXP/QSRT/resolve/main/README.md
365 Bytes
metadata
language:
- ar
The QARiB-Sentiment-Reviews-Tweets (QSRT) Model is the continuation of training from the last checkpoint of the QSR model for an additional 300k training steps (600k in total with QSR training steps). The QSRT model was trained for roughly 52 epochs using sentiment dataset contains both tweets and reviews (consists of 1,433,657 sentences).