Token Classification
Transformers
PyTorch
roberta
LABEL-0 = NONE
LABEL-1 = B-DATE
LABEL-2 = I-DATE
LABEL-3 = B-TIME
LABEL-4 = I-TIME
LABEL-5 = B-DURATION
LABEL-6 = I-DURATION
LABEL-7 = B-SET
LABEL-8 = I-SET
Instructions to use asdc/Bio-RoBERTime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asdc/Bio-RoBERTime with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="asdc/Bio-RoBERTime")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("asdc/Bio-RoBERTime") model = AutoModelForTokenClassification.from_pretrained("asdc/Bio-RoBERTime", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from asdc/Bio-RoBERTime: direct link, hf CLI and curl.
- Browser
- Download file 2.32 MB
-
https://huggingface.co/asdc/Bio-RoBERTime/resolve/main/tokenizer.json
- Command line
-
hf download hf://asdc/Bio-RoBERTime/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/asdc/Bio-RoBERTime/resolve/main/tokenizer.json
2.32 MB
File too large to display, you can check the raw version instead.