Token Classification
Transformers
PyTorch
JAX
Safetensors
English
bert
biology
microbiology
deep-learning
Instructions to use virtual-human-chc/prot_bert_bfd_ss3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use virtual-human-chc/prot_bert_bfd_ss3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="virtual-human-chc/prot_bert_bfd_ss3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("virtual-human-chc/prot_bert_bfd_ss3") model = AutoModelForTokenClassification.from_pretrained("virtual-human-chc/prot_bert_bfd_ss3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Input
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An array of uppercase letters of amino acid residues, e.g. ["PRTEINO"]
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## Output
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A list of dictionaries. The keys of the dictonaries are: `entity`, `score`, `index`, `word`, `start`, `end`. `entity` is the predicted secondary structure, score is the confidence of the model about the prediction, `index` is the position of the residue in the sequence. `word` is the residue, which the prediction is made. `start` and `end` again idetify the position of the residue. Example for a single residue: [[{'entity': 'C', 'score': np.float32(0.9825784), 'index': 1, 'word': 'M', 'start': 0, 'end': 1}]].
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## Copyright
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## Input
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An array of uppercase letters of amino acid residues, e.g. `["PRTEINO"]`
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## Output
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A list of dictionaries. The keys of the dictonaries are: `entity`, `score`, `index`, `word`, `start`, `end`. `entity` is the predicted secondary structure, score is the confidence of the model about the prediction, `index` is the position of the residue in the sequence. `word` is the residue, which the prediction is made. `start` and `end` again idetify the position of the residue. Example for a single residue: `[[{'entity': 'C', 'score': np.float32(0.9825784), 'index': 1, 'word': 'M', 'start': 0, 'end': 1}]]`.
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## Copyright
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