import torch
from transformers import T5ForConditionalGeneration,T5Tokenizer
defset_seed(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
set_seed(42)
model = T5ForConditionalGeneration.from_pretrained('valurank/t5-paraphraser')
tokenizer = T5Tokenizer.from_pretrained('valurank/t5-paraphraser')
device = torch.device("cuda"if torch.cuda.is_available() else"cpu")
print ("device ",device)
model = model.to(device)
sentence = "Which course should I take to get started in data science?"# sentence = "What are the ingredients required to bake a perfect cake?"# sentence = "What is the best possible approach to learn aeronautical engineering?"# sentence = "Do apples taste better than oranges in general?"
text = "paraphrase: " + sentence + " </s>"
max_len = 256
encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
# set top_k = 50 and set top_p = 0.95 and num_return_sequences = 3
beam_outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
do_sample=True,
max_length=256,
top_k=120,
top_p=0.98,
early_stopping=True,
num_return_sequences=10
)
print ("\nOriginal Question ::")
print (sentence)
print ("\n")
print ("Paraphrased Questions :: ")
final_outputs =[]
for beam_output in beam_outputs:
sent = tokenizer.decode(beam_output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
if sent.lower() != sentence.lower() and sent notin final_outputs:
final_outputs.append(sent)
for i, final_output inenumerate(final_outputs):
print("{}: {}".format(i, final_output))
Output
Original Question ::
Which course should I take to get started in data science?
Paraphrased Questions ::
0: What should I learn to become a data scientist?
1: How do I get started with data science?
2: How would you start a data science career?
3: How can I start learning data science?
4: How do you get started in data science?
5: What's the best course for data science?
6: Which course should I start with for data science?
7: What courses should I follow to get started in data science?
8: What degree should be taken by a data scientist?
9: Which course should I follow to become a Data Scientist?
Runs of valurank t5-paraphraser on huggingface.co
0
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
0
30-day runs
More Information About t5-paraphraser huggingface.co Model
t5-paraphraser huggingface.co is an AI model on huggingface.co that provides t5-paraphraser's model effect (), which can be used instantly with this valurank t5-paraphraser model. huggingface.co supports a free trial of the t5-paraphraser model, and also provides paid use of the t5-paraphraser. Support call t5-paraphraser model through api, including Node.js, Python, http.
t5-paraphraser huggingface.co is an online trial and call api platform, which integrates t5-paraphraser's modeling effects, including api services, and provides a free online trial of t5-paraphraser, you can try t5-paraphraser online for free by clicking the link below.
valurank t5-paraphraser online free url in huggingface.co:
t5-paraphraser is an open source model from GitHub that offers a free installation service, and any user can find t5-paraphraser on GitHub to install. At the same time, huggingface.co provides the effect of t5-paraphraser install, users can directly use t5-paraphraser installed effect in huggingface.co for debugging and trial. It also supports api for free installation.