Instructions to use tartuNLP/mmBERT-small-m-edu-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tartuNLP/mmBERT-small-m-edu-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tartuNLP/mmBERT-small-m-edu-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tartuNLP/mmBERT-small-m-edu-classifier") model = AutoModelForSequenceClassification.from_pretrained("tartuNLP/mmBERT-small-m-edu-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Trained on full documents of up to 8192 tokens in total. The train set of [tartuNLP/fineweb-c-combined-resample](https://huggingface.co/datasets/tartuNLP/fineweb-c-combined-resample)
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was used, which itself is a mix and a resample of [HuggingFaceFW/fineweb-edu-llama3-annotations](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu-llama3-annotations) and
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## Labels
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Trained on full documents of up to 8192 tokens in total. The train set of [tartuNLP/fineweb-c-combined-resample](https://huggingface.co/datasets/tartuNLP/fineweb-c-combined-resample)
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was used, which itself is a mix and a resample of [HuggingFaceFW/fineweb-edu-llama3-annotations](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu-llama3-annotations) and
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[data-is-better-together/fineweb-c](https://huggingface.co/datasets/data-is-better-together/fineweb-c).
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## Labels
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