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") - Notebooks
- Google Colab
- Kaggle
distilbert => bert; distilbert didn't exist
Browse files- config.json +2 -2
config.json
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{
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"_name_or_path": "
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"activation": "gelu",
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"architectures": [
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"
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],
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"attention_dropout": 0.1,
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"attention_probs_dropout_prob": 0.1,
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{
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"_name_or_path": "bert-base-uncased",
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"activation": "gelu",
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"architectures": [
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"BertForMultilabelSequenceClassification"
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],
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"attention_dropout": 0.1,
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"attention_probs_dropout_prob": 0.1,
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