Instructions to use MiMe-MeMo/dfm_indirect_speech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MiMe-MeMo/dfm_indirect_speech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MiMe-MeMo/dfm_indirect_speech")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("MiMe-MeMo/dfm_indirect_speech") model = AutoModelForTokenClassification.from_pretrained("MiMe-MeMo/dfm_indirect_speech") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 358448568c652e0d256797935e664ad534849ce28cb3be7cb05e007e44f24e8b
- Size of remote file:
- 5.5 kB
- SHA256:
- 59ba8a89e738bb3726c1511b54efb20958040d66826202badfec623da7fea21a
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