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
| 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. |