Description
Experiments with encoder-decoder model, where encoder is alephbert-base and decoder is pruned mT5-base model
Could be useful for generation negative and hard-negative samples for pair-text classification.
(To paraphrase is better to use classical approaches rather than this one)
Usage
git clone https://huggingface.co/imvladikon/alephbert-encoder-t5-decoder
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoModel
from transformers.modeling_outputs import BaseModelOutput
from datasets import load_dataset
enc_checkpoint = "./alephbert-encoder-t5-decoder/encoder"
enc_tokenizer = AutoTokenizer.from_pretrained(enc_checkpoint)
encoder = AutoModel.from_pretrained(enc_checkpoint).cuda()
dec_checkpoint = "./alephbert-encoder-t5-decoder/decoder"
dec_tokenizer = AutoTokenizer.from_pretrained(dec_checkpoint)
decoder = AutoModelForSeq2SeqLM.from_pretrained(dec_checkpoint).cuda()
def encode(texts):
encoded_input = enc_tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors='pt')
with torch.no_grad():
model_output = encoder(**encoded_input.to(encoder.device))
embeddings = model_output.pooler_output
embeddings = torch.nn.functional.normalize(embeddings)
return embeddings
def decode(embeddings, max_length=256, repetition_penalty=3.0, **kwargs):
out = decoder.generate(
encoder_outputs=BaseModelOutput(last_hidden_state=embeddings.unsqueeze(1)),
max_length=max_length,
repetition_penalty=repetition_penalty,
)
return [dec_tokenizer.decode(tokens, skip_special_tokens=True) for tokens in out]
encoder.eval()
text = """
诪讞专 讬讜住讬祝 诇讛讬讜转 诪注讜谞谉 讞诇拽讬转 讜讘诪讛诇讱 讛讬讜诐 讬转讞讝拽讜 讛专讜讞讜转 讘讚专讜诐 讛讗专抓 讜讬讬转讻谉 讗讜讘讱 讘讗讝讜专.
""".strip()
batch = [text]
embeddings = encode(batch)
decoder.eval()
out = decoder.generate(encoder_outputs=BaseModelOutput(last_hidden_state=embeddings.unsqueeze(1)), max_length=512, repetition_penalty=3.0)
for t, o in zip(batch, out):
print(t)
print(dec_tokenizer.decode(o, skip_special_tokens=True))
print('-----------')
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