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---
annotations_creators: []
language:
- ro
language_creators:
- machine-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: BlackKakapo/t5-small-paraphrase-ro
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- text2text-generation
task_ids: []
---
# Romanian paraphrase
Fine-tune t5-small model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own [dataset](https://huggingface.co/datasets/BlackKakapo/paraphrase-ro-v1). The dataset contains ~60k examples.
### How to use
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("BlackKakapo/t5-small-paraphrase-ro")
model = AutoModelForSeq2SeqLM.from_pretrained("BlackKakapo/t5-small-paraphrase-ro")
```
### Or
```python
from transformers import T5ForConditionalGeneration, T5TokenizerFast
model = T5ForConditionalGeneration.from_pretrained("BlackKakapo/t5-small-paraphrase-ro")
tokenizer = T5TokenizerFast.from_pretrained("BlackKakapo/t5-small-paraphrase-ro")
```
### Generate
```python
text = "Am impresia că fac multe greșeli."
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)
beam_outputs = model.generate(
input_ids=input_ids,
attention_mask=attention_masks,
do_sample=True,
max_length=256,
top_k=10,
top_p=0.9,
early_stopping=False,
num_return_sequences=5
)
for beam_output in beam_outputs:
text_para = tokenizer.decode(beam_output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
if text.lower() != text_para.lower() or text not in final_outputs:
final_outputs.append(text_para)
break
print(final_outputs)
```
### Output
```out
['Cred că fac multe greșeli.']
``` |