
```python
from transformers import T5ForConditionalGeneration
from transformers import T5TokenizerFast as T5Tokenizer
model = "svjack/summary-dialogue-eng"
device = "cpu"
tokenizer = T5Tokenizer.from_pretrained(model)
model = T5ForConditionalGeneration.from_pretrained(model).to(device).eval()

prompt =  "The Wisconsin Territorial Centennial half dollar was designed by David Parsons and Benjamin Hawkins and minted by the United States Bureau of the Mint in 1936. The obverse (pictured) depicts a pick axe and lead ore, referring to the lead mining in early Wisconsin"
prompt = "{}\nCandidates:Tom Jack".format(prompt)

encode = tokenizer(prompt, return_tensors='pt').to(device)
answer = model.generate(encode.input_ids,
                       max_length = 128,
    num_beams=2,
    top_p = 0.95,
    top_k = 50,
    repetition_penalty = 2.5,
    length_penalty=1.0,
    early_stopping=True,
                       )[0]
decoded = tokenizer.decode(answer, skip_special_tokens=True)
decoded.replace("Tom:", "\n").replace("Jack:", "\n").split("\n")
```

</br>

```json
['',
 ' Have you seen the Wisconsin Territorial Centennial half dollar? ',
 ' Yeah, it was designed by David Parsons and Benjamin Hawkins. ',
 ' What is it? ',
 " It's a half dollar with a pick axe and lead ore. ",
 " That's great!"]
```
{"language": ["en"], "tags": ["t5"], "pipeline_tag": "text2text-generation"}
transformers pytorch t5 text2text-generation en autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 




</br>


TAGS
#transformers #pytorch #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

TAGS
#transformers #pytorch #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
