Instructions to use AbrorBalxiyev/checking-equal-meaning-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbrorBalxiyev/checking-equal-meaning-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AbrorBalxiyev/checking-equal-meaning-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AbrorBalxiyev/checking-equal-meaning-model") model = AutoModelForSequenceClassification.from_pretrained("AbrorBalxiyev/checking-equal-meaning-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d758b99ff7cb1725becc43349c44da68ad3e4c2ffa640ba54f97521e238e7745
- Size of remote file:
- 438 MB
- SHA256:
- 1fb960462ecce89d680dd454823ffb442cb1f97d3e288973fc29a70b7a2442bd
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