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:
- 9fb8a035c5c721630271580702d95cd0fc992918ea1146f6998984139acff5f1
- Size of remote file:
- 4.6 kB
- SHA256:
- e49f57341bdd54c8d76d33cda79add6c34d93f58ac975eff30d2ac8978768845
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