Instructions to use openeurollm/bsc-edu-annotator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openeurollm/bsc-edu-annotator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="openeurollm/bsc-edu-annotator")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("openeurollm/bsc-edu-annotator") model = AutoModel.from_pretrained("openeurollm/bsc-edu-annotator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from openeurollm/bsc-edu-annotator: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/openeurollm/bsc-edu-annotator/resolve/main/tokenizer.json
- Command line
-
hf download hf://openeurollm/bsc-edu-annotator/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/openeurollm/bsc-edu-annotator/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 3d3a9ab194466b6e3502faf1baa7aa66ed010b9dc04a580da3c830849b64c149
- Size of remote file:
- 17.1 MB
- SHA256:
- 33cd99e33ce09bdd8a6136fddfe90a1c47f85bafedf7309d0eecc19012d43586
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