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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cdb4f14aca0525633a35c20385415a00af9f0c68084981291c2836830ebcd18b
- Size of remote file:
- 17.1 MB
- SHA256:
- d2b0d724d49f4a6585a731a273eba859f3406c977aaed9247b2d17e97d585580
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