Instructions to use asdc/XLM_Temporal_Expression_Normalization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asdc/XLM_Temporal_Expression_Normalization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="asdc/XLM_Temporal_Expression_Normalization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("asdc/XLM_Temporal_Expression_Normalization") model = AutoModelForMaskedLM.from_pretrained("asdc/XLM_Temporal_Expression_Normalization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from asdc/XLM_Temporal_Expression_Normalization: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/asdc/XLM_Temporal_Expression_Normalization/resolve/main/model.safetensors
- Command line
-
hf download hf://asdc/XLM_Temporal_Expression_Normalization/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/asdc/XLM_Temporal_Expression_Normalization/resolve/main/model.safetensors
2.24 GB
- Xet hash:
- 7e7e8d92e22c5e11712150811b836e076f6a055eaf81104de984d5a1689f96b1
- Size of remote file:
- 2.24 GB
- SHA256:
- 735e4c0f1a2fb13303e2302fb592a2e13ff6a249f2514594b697f3f8a8c19e71
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.