Instructions to use NLP-EXP/QE6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NLP-EXP/QE6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NLP-EXP/QE6")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NLP-EXP/QE6") model = AutoModelForMaskedLM.from_pretrained("NLP-EXP/QE6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from NLP-EXP/QE6: direct link, hf CLI and curl.
- Browser
- Download file 324 Bytes
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https://huggingface.co/NLP-EXP/QE6/resolve/main/README.md
- Command line
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hf download hf://NLP-EXP/QE6/README.md
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curl -L -o README.md https://huggingface.co/NLP-EXP/QE6/resolve/main/README.md
324 Bytes
metadata
language:
- ar
The QARiB-Emotion-600k (QE6) model was trained by continuing the training from the last checkpoint of the QE3 model using the emotion tweets dataset (roughly 1.2 million tweets). The QE6 model trained for an additional 300k steps (600k in total including the QE3 training steps) and around 31 epochs.