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import nltk
import torch
import numpy as np
import gradio as gr
from nltk import sent_tokenize
from transformers import RobertaTokenizer, RobertaForMaskedLM
nltk.download('punkt')
cuda = torch.cuda.is_available()
tokenizer = RobertaTokenizer.from_pretrained("roberta-large")
model = RobertaForMaskedLM.from_pretrained("roberta-large")
if cuda:
model = model.cuda()
max_len = 20
top_k = 100
temperature = 1
burnin = 250
max_iter = 500
# adapted from https://github.com/nyu-dl/bert-gen
def generate_step(out,
gen_idx,
temperature=None,
top_k=0,
sample=False,
return_list=True):
""" Generate a word from from out[gen_idx]
args:
- out (torch.Tensor): tensor of logits of size batch_size x seq_len x vocab_size
- gen_idx (int): location for which to generate for
- top_k (int): if >0, only sample from the top k most probable words
- sample (Bool): if True, sample from full distribution. Overridden by top_k
"""
logits = out.logits[:, gen_idx]
if temperature is not None:
logits = logits / temperature
if top_k > 0:
kth_vals, kth_idx = logits.topk(top_k, dim=-1)
dist = torch.distributions.categorical.Categorical(logits=kth_vals)
idx = kth_idx.gather(dim=1,
index=dist.sample().unsqueeze(-1)).squeeze(-1)
elif sample:
dist = torch.distributions.categorical.Categorical(logits=logits)
idx = dist.sample() # removed superfluous squeeze(-1)
else:
idx = torch.argmax(logits, dim=-1)
return idx.tolist() if return_list else idx
# adapted from https://github.com/nyu-dl/bert-gen
def parallel_sequential_generation(seed_text,
seed_end_text,
max_len=max_len,
top_k=top_k,
temperature=temperature,
max_iter=max_iter,
burnin=burnin):
""" Generate for one random position at a timestep
args:
- burnin: during burn-in period, sample from full distribution; afterwards take argmax
"""
inp = tokenizer(seed_text + tokenizer.mask_token * max_len + seed_end_text,
return_tensors='pt')
masked_tokens = np.where(
inp['input_ids'][0].numpy() == tokenizer.mask_token_id)[0]
seed_len = masked_tokens[0]
if cuda:
inp = inp.to('cuda')
for ii in range(max_iter):
kk = np.random.randint(0, max_len)
out = model(**inp)
topk = top_k if (ii >= burnin) else 0
idxs = generate_step(out,
gen_idx=seed_len + kk,
top_k=topk,
temperature=temperature,
sample=(ii < burnin))
inp['input_ids'][0][seed_len + kk] = idxs[0]
tokens = inp['input_ids'].cpu().numpy()[0][masked_tokens]
tokens = tokens[(np.where((tokens != tokenizer.eos_token_id)
& (tokens != tokenizer.bos_token_id)))]
return tokenizer.decode(tokens)
def inbertolate(doc,
max_len=15,
top_k=0,
temperature=None,
max_iter=300,
burnin=200):
new_doc = ''
paras = doc.split('\n')
for para in paras:
para = sent_tokenize(para)
if para == '':
new_doc += '\n'
continue
para += ['']
for sentence in range(len(para) - 1):
new_doc += para[sentence] + ' '
new_doc += parallel_sequential_generation(para[sentence],
para[sentence + 1],
max_len=max_len,
top_k=top_k,
temperature=temperature,
burnin=burnin,
max_iter=max_iter) + ' '
new_doc += '\n'
return new_doc
if __name__ == '__main__':
block = gr.Blocks(css='.container')
with block:
gr.Markdown("<h1><center>inBERTolate</center></h1>")
gr.Markdown(
"<center>Hit your word count by using BERT to pad out your essays!</center>"
)
gr.Interface(
fn=inbertolate,
inputs=[
gr.Textbox(label="Text", lines=7),
gr.Slider(label="Maximum length to insert between sentences",
minimum=1,
maximum=40,
step=1,
value=max_len),
gr.Slider(label="Top k", minimum=0, maximum=200, value=top_k),
gr.Slider(label="Temperature",
minimum=0,
maximum=2,
value=temperature),
gr.Slider(label="Maximum iterations",
minimum=0,
maximum=1000,
value=max_iter),
gr.Slider(label="Burn-in",
minimum=0,
maximum=500,
value=burnin),
],
outputs=gr.Textbox(label="Expanded text", lines=24))
block.launch(server_name='0.0.0.0')
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