| | --- |
| | |
| | license: apache-2.0 |
| | inference: false |
| |
|
| | --- |
| | |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelWithLMHead, AutoModelForCausalLM |
| | import torch |
| | if torch.cuda.is_available(): |
| | device = torch.device("cuda") |
| | else : |
| | device = "cpu" |
| | |
| | |
| | tokenizer = AutoTokenizer.from_pretrained("Ashishkr/grammar_correction") |
| | model = AutoModelForCausalLM.from_pretrained("Ashishkr/grammar_correction").to(device) |
| | |
| | input_query="what be the reason for everyone leave the company" |
| | query= "<|startoftext|> " + 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= 0.7, |
| | top_k = 5, |
| | num_return_sequences=3) |
| | corrected_sentences = [] |
| | 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 corrected_sentences: |
| | corrected_sentences.append(r) |
| | |
| | print(corrected_sentences) |
| | ``` |
| |
|
