Instructions to use ncardus/TrainedMLMBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncardus/TrainedMLMBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ncardus/TrainedMLMBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ncardus/TrainedMLMBERT") model = AutoModel.from_pretrained("ncardus/TrainedMLMBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"position_embedding_type": "
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"torch_dtype": "float32",
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"transformers_version": "4.31.0",
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"type_vocab_size": 1,
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"position_embedding_type": "relative_key",
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"torch_dtype": "float32",
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"transformers_version": "4.31.0",
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"type_vocab_size": 1,
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pytorch_model.bin
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size 345160401
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