Text Classification
Transformers
Safetensors
English
esm
protein-classification
bioinformatics
anticancer
esm2
torch
Instructions to use raghavagps-group/anticp3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raghavagps-group/anticp3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raghavagps-group/anticp3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("raghavagps-group/anticp3") model = AutoModelForSequenceClassification.from_pretrained("raghavagps-group/anticp3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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import torch
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# Load tokenizer and fine-tuned model
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tokenizer = AutoTokenizer.from_pretrained("
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model = AutoModelForSequenceClassification.from_pretrained("
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# Example protein sequence
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sequence = "MANCVVGYIGERCQYRDLKWWELRGGGGSGGGGSAPAFSVSPASGLSDGQSVSVSVSGAAAGETYYIAQCAPVGGQDACNPATATSFTTDASGAASFSFVVRKSYTGSTPEGTPVGSVDCATAACNLGAGNSGLDLGHVALTFGGGGGSGGGGSDHYNCVSSGGQCLYSACPIFTKIQGTCYRGKAKCCKLEHHHHHH"
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import torch
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# Load tokenizer and fine-tuned model
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tokenizer = AutoTokenizer.from_pretrained("raghavagps-group/anticp3")
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model = AutoModelForSequenceClassification.from_pretrained("raghavagps-group/anticp3")
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# Example protein sequence
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sequence = "MANCVVGYIGERCQYRDLKWWELRGGGGSGGGGSAPAFSVSPASGLSDGQSVSVSVSGAAAGETYYIAQCAPVGGQDACNPATATSFTTDASGAASFSFVVRKSYTGSTPEGTPVGSVDCATAACNLGAGNSGLDLGHVALTFGGGGGSGGGGSDHYNCVSSGGQCLYSACPIFTKIQGTCYRGKAKCCKLEHHHHHH"
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