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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload config
Browse files- config.json +27 -0
config.json
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{
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"_name_or_path": "models/bpe/roformer/",
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"architectures": [
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"RoFormerForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"directionality": "bidi",
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"embedding_size": 768,
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.01,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 1026,
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"model_type": "roformer",
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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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"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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"vocab_size": 33
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}
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