Instructions to use DAMO-NLP-SG/roberta-time_identification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DAMO-NLP-SG/roberta-time_identification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="DAMO-NLP-SG/roberta-time_identification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-SG/roberta-time_identification") model = AutoModelForTokenClassification.from_pretrained("DAMO-NLP-SG/roberta-time_identification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from DAMO-NLP-SG/roberta-time_identification: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/DAMO-NLP-SG/roberta-time_identification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://DAMO-NLP-SG/roberta-time_identification/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/DAMO-NLP-SG/roberta-time_identification/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- 2a1fc9f2417fadff2f0a1a551be01256118dbbb50bd74beeaf4f023b6f307fbf
- Size of remote file:
- 1.42 GB
- SHA256:
- c2fe8d376a6c22f7c59e578a9e6e540bd1e1f9a8cbecab9aaa9f60076c832b44
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