# Interest extraction
  Extracts the interests from a question-answer pair.
### Model input
> [QUESTION]\
> [ANSWER]
### Example
> What do you like to do in the weekend?\
  I like to spend my free time reading, playing video games, and going on walks.

### Output
> reading, video games, walking

### How to use in code
```{python}
import nltk
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("njvdnbus/Interest_extraction")
model = AutoModelForSeq2SeqLM.from_pretrained("njvdnbus/Interest_extraction")

def use_model(text):
    inputs = ["" + text]
    inputs = tokenizer(inputs, truncation=True, return_tensors="pt")
    output = model.generate(**inputs, num_beams=1, do_sample=True, min_length=1, max_length=64)
    decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
    predicted_interests = nltk.sent_tokenize(decoded_output.strip())[0]
    return predicted_interests

text= '''What other hobbies do you have?
When I have time I like to cook for my family. Most often this only happens in the weekends.'''
print(use_model(text))
```

> cooking

{"language": ["en"], "metrics": ["f1"]}
transformers pytorch t5 text2text-generation en autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

# Interest extraction
  Extracts the interests from a question-answer pair.
### Model input
> [QUESTION]\
> [ANSWER]
### Example
> What do you like to do in the weekend?\
  I like to spend my free time reading, playing video games, and going on walks.

### Output
> reading, video games, walking

### How to use in code


> cooking

# Interest extraction
  Extracts the interests from a question-answer pair. ### Model input
> [QUESTION]\
> [ANSWER] ### Example
> What do you like to do in the weekend?\
  I like to spend my free time reading, playing video games, and going on walks. ### Output
> reading, video games, walking ### How to use in code


> cooking
TAGS
#transformers #pytorch #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Interest extraction
  Extracts the interests from a question-answer pair. ### Model input
> [QUESTION]\
> [ANSWER] ### Example
> What do you like to do in the weekend?\
  I like to spend my free time reading, playing video games, and going on walks. ### Output
> reading, video games, walking ### How to use in code


> cooking
TAGS
#transformers #pytorch #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Interest extraction
  Extracts the interests from a question-answer pair.### Model input
> [QUESTION]\
> [ANSWER]### Example
> What do you like to do in the weekend?\
  I like to spend my free time reading, playing video games, and going on walks.### Output
> reading, video games, walking### How to use in code


> cooking