Spaces:
Running
on
Zero
Running
on
Zero
Faezeh Sarlakifar
commited on
Commit
·
4745b4a
1
Parent(s):
d8f5373
Update esm embedder function
Browse files
app.py
CHANGED
@@ -4,6 +4,7 @@ import numpy as np
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from transformers import T5Tokenizer, T5EncoderModel
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import esm
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from inference import load_models, predict_ensemble
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# Load trained models
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model_protT5, model_cat = load_models()
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@@ -13,11 +14,10 @@ tokenizer_t5 = T5Tokenizer.from_pretrained("Rostlab/prot_t5_xl_uniref50", do_low
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model_t5 = T5EncoderModel.from_pretrained("Rostlab/prot_t5_xl_uniref50")
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model_t5 = model_t5.eval()
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# Load
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esm_model.
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def extract_prott5_embedding(sequence):
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sequence = sequence.replace(" ", "")
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@@ -28,14 +28,21 @@ def extract_prott5_embedding(sequence):
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return torch.mean(embedding, dim=1)
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def extract_esm_embedding(sequence):
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with torch.no_grad():
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return torch.mean(token_representations[0, 1:len(sequence)+1], dim=0).unsqueeze(0)
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def classify(sequence):
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protT5_emb = extract_prott5_embedding(sequence)
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esm_emb = extract_esm_embedding(sequence)
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from transformers import T5Tokenizer, T5EncoderModel
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import esm
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from inference import load_models, predict_ensemble
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from transformers import AutoTokenizer, AutoModel
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# Load trained models
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model_protT5, model_cat = load_models()
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model_t5 = T5EncoderModel.from_pretrained("Rostlab/prot_t5_xl_uniref50")
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model_t5 = model_t5.eval()
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# Load the tokenizer and model
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model_name = "facebook/esm2_t33_650M_UR50D"
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tokenizer_esm = AutoTokenizer.from_pretrained(model_name)
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esm_model = AutoModel.from_pretrained(model_name)
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def extract_prott5_embedding(sequence):
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sequence = sequence.replace(" ", "")
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return torch.mean(embedding, dim=1)
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# Extract ESM2 embedding
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def extract_esm_embedding(sequence):
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# Tokenize the sequence
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inputs = tokenizer_esm(sequence, return_tensors="pt", padding=True, truncation=True)
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# Forward pass through the model
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with torch.no_grad():
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outputs = esm_model(**inputs)
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# Extract the embeddings from the 33rd layer (ESM2 layer)
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token_representations = outputs.last_hidden_state # This is the default layer
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return torch.mean(token_representations[0, 1:len(sequence)+1], dim=0).unsqueeze(0)
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def classify(sequence):
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protT5_emb = extract_prott5_embedding(sequence)
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esm_emb = extract_esm_embedding(sequence)
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