Instructions to use MU-NLPC/XLM-R-large-reflective-conf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MU-NLPC/XLM-R-large-reflective-conf4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MU-NLPC/XLM-R-large-reflective-conf4", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MU-NLPC/XLM-R-large-reflective-conf4") model = AutoModelForSequenceClassification.from_pretrained("MU-NLPC/XLM-R-large-reflective-conf4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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- **Repository:** https://github.com/EduMUNI/reflection-classification
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- **Paper:** https://link.springer.com/article/10.1007/s10639-022-11254-7
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- **Developed by:**
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- **Model type:** Roberta-large
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- **Finetuned from model:** [XLM-R-large](https://huggingface.co/xlm-roberta-large)
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- **Repository:** https://github.com/EduMUNI/reflection-classification
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- **Paper:** https://link.springer.com/article/10.1007/s10639-022-11254-7
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- **Developed by:** Michal Stefanik & Jan Nehyba, Masaryk University
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- **Model type:** Roberta-large
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- **Finetuned from model:** [XLM-R-large](https://huggingface.co/xlm-roberta-large)
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