| --- |
| base_model: |
| - leaBroe/HeavyBERTa |
| - leaBroe/LightGPT |
| pipeline_tag: translation |
| --- |
| # Heavy2Light |
| 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. The model utilizes [Adapters](https://github.com/adapter-hub/adapters) for efficient fine-tuning. You can either download the full model weights and adapter from this repository, or directly use the Heavy2Light adapter available in its dedicated [directory](https://huggingface.co/leaBroe/Heavy2Light_adapter) on Hugging Face. |
| For more information, please visit our GitHub [repository](https://github.com/ibmm-unibe-ch/Heavy2Light.git). |
|
|
| ## How to use the model |
| ```python |
| from transformers import EncoderDecoderModel, AutoTokenizer, GenerationConfig |
| from adapters import init |
| |
| model_path = "leaBroe/Heavy2Light" |
| subfolder_path = "heavy2light_final_checkpoint" |
| |
| model = EncoderDecoderModel.from_pretrained(model_path) |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_path, subfolder=subfolder_path) |
| |
| init(model) |
| adapter_name = model.load_adapter("leaBroe/Heavy2Light_adapter", set_active=True) |
| model.set_active_adapters(adapter_name) |
| |
| generation_config = GenerationConfig.from_pretrained(model_path) |
| |
| # example input heavy sequence |
| heavy_seq = "QLQVQESGPGLVKPSETLSLTCTVSGASSSIKKYYWGWIRQSPGKGLEWIGSIYSSGSTQYNPALGSRVTLSVDTSQTQFSLRLTSVTAADTATYFCARQGADCTDGSCYLNDAFDVWGRGTVVTVSS" |
| |
| inputs = tokenizer( |
| heavy_seq, |
| padding="max_length", |
| truncation=True, |
| max_length=250, |
| return_tensors="pt" |
| ) |
| |
| generated_seq = model.generate( |
| input_ids=inputs.input_ids, |
| attention_mask=inputs.attention_mask, |
| num_return_sequences=1, |
| output_scores=True, |
| return_dict_in_generate=True, |
| generation_config=generation_config, |
| bad_words_ids=[[4]], |
| do_sample=True, |
| temperature=1.0, |
| ) |
| |
| generated_text = tokenizer.decode( |
| generated_seq.sequences[0], |
| skip_special_tokens=True, |
| ) |
| |
| print("Generated light sequence:", generated_text) |
| ``` |
