Instructions to use ai4data/datause-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ai4data/datause-classifier with PEFT:
Task type is invalid.
- Transformers
How to use ai4data/datause-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ai4data/datause-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ai4data/datause-classifier", device_map="auto") - GLiNER
How to use ai4data/datause-classifier with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("ai4data/datause-classifier") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from ai4data/datause-classifier: direct link, hf CLI and curl.
- Browser
- Download file 31.8 MB
-
https://huggingface.co/ai4data/datause-classifier/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://ai4data/datause-classifier/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/ai4data/datause-classifier/resolve/main/adapter_model.safetensors
31.8 MB
- Xet hash:
- 002fe9677c14cd299bcb9256ec517411c8cbfc413911c87e85be0bd20c2b1acc
- Size of remote file:
- 31.8 MB
- SHA256:
- b77e6167c849c75d551e78e59fd150932ee67bbb9a8d3f230f13f1bca2b9f593
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.