Instructions to use ncardus/TrainedMLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncardus/TrainedMLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ncardus/TrainedMLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ncardus/TrainedMLM") model = AutoModelForMaskedLM.from_pretrained("ncardus/TrainedMLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload BertForMaskedLM
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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"pad_token_id": 0,
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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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"pad_token_id": 0,
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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 347532069
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version https://git-lfs.github.com/spec/v1
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size 347532069
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