Use pipeline on fine tuned model
# ❓ Questions & Help

<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
     new models and benchmarks, and migration questions. For all other questions,
     we direct you to the Hugging Face forum: https://discuss.huggingface.co/ .
     You can also try Stack Overflow (SO) where a whole community of PyTorch and
     Tensorflow enthusiast can help you out. In this case, make sure to tag your
     question with the right deep learning framework as well as the
     huggingface-transformers tag: 
     https://stackoverflow.com/questions/tagged/huggingface-transformers 
     -->
## Details
I have fine tuned 'roberta-large' model according to my dataset. It is a sequence classification task
```
MODEL_NAME = 'roberta-large'
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) 
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)

# Prediction function
def predict(sent):
    sequence = tokenizer.encode_plus(sent, return_tensors="pt")['input_ids'].to(device)
    logits = model(sequence)[0]
```
The above works fine but now I would like to use this model in pipeline like we have one for question-answering
```
nlp = pipeline('question-answering', model='distilbert-base-cased-distilled-squad', tokenizer='bert-base-cased')
```
Any example would help.
Thank You.

<!-- Description of your issue -->

<!-- You should first ask your question on the forum or SO, and only if
     you didn't get an answer ask it here on GitHub. -->
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
