Instructions to use AI4Protein/deep_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4Protein/deep_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AI4Protein/deep_base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AI4Protein/deep_base") model = AutoModelForMaskedLM.from_pretrained("AI4Protein/deep_base") - Notebooks
- Google Colab
- Kaggle
Upload RoFormerForMaskedLM
Browse files- config.json +1 -0
- generation_config.json +6 -0
- pytorch_model.bin +3 -0
config.json
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"pad_token_id": 0,
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"pooler_activation": "linear",
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"rotary_value": false,
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"transformers_version": "4.31.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"pad_token_id": 0,
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"pooler_activation": "linear",
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"rotary_value": false,
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"torch_dtype": "float32",
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"transformers_version": "4.31.0",
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"type_vocab_size": 2,
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"use_cache": true,
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generation_config.json
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{
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"_from_model_config": true,
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"eos_token_id": 2,
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"pad_token_id": 0,
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"transformers_version": "4.31.0"
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:2f845c2ae5a31b81829adf40933979dd2827455b532e6f0e7ec78b0dfa03d3e4
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size 343029749
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