Token Classification
Transformers
Safetensors
Ancient Greek (to 1453)
roberta
named-entity-recognition
ner
information-extraction
ancient-greek
greek-ner
papyrology
epigraphy
classics
classical-studies
digital-humanities
economic-history
greberta
Eval Results (legacy)
Instructions to use ainouche-abderahmane/grammateus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ainouche-abderahmane/grammateus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ainouche-abderahmane/grammateus")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ainouche-abderahmane/grammateus") model = AutoModelForTokenClassification.from_pretrained("ainouche-abderahmane/grammateus", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e3cf8f6dabd1052f2a13eef3b89c469a564081325fb30083dd694eca4debc9a
- Size of remote file:
- 5.2 kB
- SHA256:
- db1efceddf9844f080c0075e01b5f5da96d5f3182ae49ae35db191a19d99b3f4
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