Instructions to use Colorful/RTA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Colorful/RTA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Colorful/RTA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Colorful/RTA") model = AutoModelForSequenceClassification.from_pretrained("Colorful/RTA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Colorful/RTA: direct link, hf CLI and curl.
- Browser
- Download file 291 Bytes
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https://huggingface.co/Colorful/RTA/resolve/refs%2Fpr%2F1/README.md
- Command line
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hf download hf://Colorful/RTA@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/Colorful/RTA/resolve/refs%2Fpr%2F1/README.md
291 Bytes
metadata
license: mit
RTA (RepresentThemAll) is a pre-trained language model for bug reports. It can be fine-tuned on all kinds of automated software maintenance tasks associated with bug reports such as bug report summarization, duplicate bug report detection, bug priority prediction, etc.