Instructions to use TCMVince/HOP4NLP5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TCMVince/HOP4NLP5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="TCMVince/HOP4NLP5", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("TCMVince/HOP4NLP5", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +2 -1
config.json
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"attention_probs_dropout_prob": 0.0,
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"auto_map": {
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"AutoConfig": "hf_configuration.BertEnergyConfig",
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"AutoModelForMaskedLM": "mlm.BertEnergyModelForMaskedLM"
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},
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"beta": null,
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"bias": true,
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"attention_probs_dropout_prob": 0.0,
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"auto_map": {
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"AutoConfig": "hf_configuration.BertEnergyConfig",
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"AutoModelForMaskedLM": "mlm.BertEnergyModelForMaskedLM",
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"AutoModelForSequenceClassification": "mlm.BertEnergyModelForSequenceClassification"
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},
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"beta": null,
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"bias": true,
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