Instructions to use emekaboris/autonlp-txc-17923129 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emekaboris/autonlp-txc-17923129 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emekaboris/autonlp-txc-17923129")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("emekaboris/autonlp-txc-17923129") model = AutoModelForSequenceClassification.from_pretrained("emekaboris/autonlp-txc-17923129") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f78a99d4f304349cf2affde6d5de0ba8881ecd131560e9324283c507dcaca29
- Size of remote file:
- 5.92 kB
- SHA256:
- e98ec9d304f72dcdba397e86131f51d6f4bf6b6a39b151563742067c1ebb2a78
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