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README.md
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- leaBroe/HeavyBERTa
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- leaBroe/LightGPT
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pipeline_tag: translation
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---
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- leaBroe/HeavyBERTa
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- leaBroe/LightGPT
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pipeline_tag: translation
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---
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# Heavy2Light
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Heavy2Light is an seq2seq model designed to generate light chain antibody sequences from corresponding heavy chain inputs. It leverages [HeavyBERTa](https://huggingface.co/leaBroe/HeavyBERTa) as the encoder and [LightGPT](https://huggingface.co/leaBroe/LightGPT) as the decoder. The model is fine-tuned on paired antibody chain data from the [OAS](https://opig.stats.ox.ac.uk/webapps/oas/) and [PlabDab](https://opig.stats.ox.ac.uk/webapps/plabdab/) databases.
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For more information, please visit our GitHub [repository](https://github.com/ibmm-unibe-ch/Heavy2Light.git).
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## How to use the model
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```python
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from transformers import EncoderDecoderModel, AutoTokenizer, GenerationConfig
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from adapters import init
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model_path = "leaBroe/Heavy2Light"
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subfolder_path = "heavy2light_final_checkpoint"
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model = EncoderDecoderModel.from_pretrained(model_path)
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tokenizer = AutoTokenizer.from_pretrained(model_path, subfolder=subfolder_path)
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init(model)
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adapter_name = model.load_adapter("leaBroe/Heavy2Light_adapter", set_active=True)
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model.set_active_adapters(adapter_name)
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generation_config = GenerationConfig.from_pretrained(model_path)
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# example input heavy sequence
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heavy_seq = "QLQVQESGPGLVKPSETLSLTCTVSGASSSIKKYYWGWIRQSPGKGLEWIGSIYSSGSTQYNPALGSRVTLSVDTSQTQFSLRLTSVTAADTATYFCARQGADCTDGSCYLNDAFDVWGRGTVVTVSS"
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inputs = tokenizer(
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heavy_seq,
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padding="max_length",
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truncation=True,
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max_length=250,
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return_tensors="pt"
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)
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generated_seq = model.generate(
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input_ids=inputs.input_ids,
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attention_mask=inputs.attention_mask,
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num_return_sequences=1,
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output_scores=True,
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return_dict_in_generate=True,
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generation_config=generation_config,
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bad_words_ids=[[4]],
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do_sample=True,
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temperature=1.0,
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)
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generated_text = tokenizer.decode(
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generated_seq.sequences[0],
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skip_special_tokens=True,
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)
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print("Generated light sequence:", generated_text)
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```
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