|
--- |
|
language: en |
|
thumbnail: https://salesken.ai/assets/images/logo.png |
|
license: apache-2.0 |
|
inference: false |
|
widget: |
|
- text: "every moment is a fresh beginning" |
|
|
|
tags: salesken |
|
--- |
|
|
|
Use this model to generate variations to augment the training data used for NLU systems. |
|
```python |
|
from transformers import AutoTokenizer, AutoModelWithLMHead |
|
|
|
import torch |
|
if torch.cuda.is_available(): |
|
device = torch.device("cuda") |
|
else : |
|
device = "cpu" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("salesken/paraphrase_generation") |
|
model = AutoModelWithLMHead.from_pretrained("salesken/paraphrase_generation").to(device) |
|
|
|
input_query="every moment is a fresh beginning" |
|
query= input_query + " ~~ " |
|
|
|
input_ids = tokenizer.encode(query.lower(), return_tensors='pt').to(device) |
|
sample_outputs = model.generate(input_ids, |
|
do_sample=True, |
|
num_beams=1, |
|
max_length=128, |
|
temperature=0.9, |
|
top_p= 0.99, |
|
top_k = 30, |
|
num_return_sequences=40) |
|
paraphrases = [] |
|
for i in range(len(sample_outputs)): |
|
r = tokenizer.decode(sample_outputs[i], skip_special_tokens=True).split('||')[0] |
|
r = r.split(' ~~ ')[1] |
|
if r not in paraphrases: |
|
paraphrases.append(r) |
|
|
|
print(paraphrases) |
|
|
|
|
|
``` |
|
|
|
|
|
To evaluate if a paraphrase is a semantic variation to the input query or just a surface level variation & rank the generated paraphrases, use the following model: |
|
|
|
https://huggingface.co/salesken/paraphrase_diversity_ranker |
|
|
|
|
|
|
