IndexError in generate protein notebook #152
Comments
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Hi, thanks for your interest in our models! :) |
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Hi @mheinzinger thanks for your help. I'm interested in generating protein sequences. Perhaps you could help me navigate my options. I started with the provided notebook only because that's where users tend to go when they are trying to get something up and running. I didn't use ProtT5 because it is not an example in Since you highly recommend ProtT5, I propose we remove XLNet generate script and replace it with one that both works and uses a recommended model. If you could help me modify the notebook, I'd be happy to make a PR. Does this sound like a plan to you? If so, here's what I've done so far: import torch
-from transformers import XLNetLMHeadModel, XLNetTokenizer,pipeline
+from transformers import T5Tokenizer, pipeline, T5ForConditionalGeneration
import re
import os
import requests
from tqdm.auto import tqdm
-tokenizer = XLNetTokenizer.from_pretrained("Rostlab/prot_xlnet", do_lower_case=False)
+tokenizer = T5Tokenizer.from_pretrained("Rostlab/prot_t5_xl_uniref50", do_lower_case=False)
-model = XLNetLMHeadModel.from_pretrained("Rostlab/prot_xlnet")
+# TypeError: T5ForConditionalGeneration.__init__() got an unexpected keyword argument 'do_lower_case'
+model = T5ForConditionalGeneration.from_pretrained("Rostlab/prot_t5_xl_uniref50")
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
model = model.eval()
sequences_Example = "A E T C Z A O"
sequences_Example = re.sub(r"[UZOB]", "<unk>", sequences_Example)
ids = tokenizer.encode(sequences_Example, add_special_tokens=False)
input_ids = torch.tensor(ids).unsqueeze(0).to(device)
max_length = 100
temperature = 1.0
k = 0
p = 0.9
repetition_penalty = 1.0
num_return_sequences = 3
output_ids = model.generate(
input_ids=input_ids,
max_length=max_length,
temperature=temperature,
top_k=k,
top_p=p,
repetition_penalty=repetition_penalty,
do_sample=True,
num_return_sequences=num_return_sequences,
)
output_sequences = [" ".join(" ".join(tokenizer.decode(output_id)).split()) for output_id in output_ids]
print('Generated Sequences\n')
for output_sequence in output_sequences:
print(output_sequence)
+# Output:
+# < p a d > A E T C P A < / s >
+# < p a d > A E T C P A < / s >
+# < p a d > A E T C R A < / s >Since the T5 model supports conditional generation, I think it would be nice to see a few examples for how that works. |
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You are perfectly right, unfortunately, I did not find the time yet to update the examples. |
Hello,
Thanks for the great tool. I'm excited to use ProtTrans for generating protein sequences, but I'm getting an index error in the example notebook (https://github.com/agemagician/ProtTrans/blob/master/Generate/ProtXLNet.ipynb).
The error occurs when running cell 12:
Here's the full traceback:
This has occurred on two different sets of hardware.
Thanks for taking a look.
Evan
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