Instructions to use Pendrokar/TorchMoji with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pendrokar/TorchMoji with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Pendrokar/TorchMoji")# Load model directly from transformers import AutoTokenizer, BertForMultilabelSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Pendrokar/TorchMoji") model = BertForMultilabelSequenceClassification.from_pretrained("Pendrokar/TorchMoji", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d677cffcc4990cf7b098b861c562c59816dc87e8514065ad0ffb01fbaeca322c
- Size of remote file:
- 89.6 MB
- SHA256:
- 8cbf6f7067d56aa1c2d571bb169f05fba16cea4c263c06fb3f217f42c591a978
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