Lots-of-LoRAs/task957_e2e_nlg_text_generation_generate huggingface dataset:
Definition: In this task, we ask you convert a data table of restaurant descriptions into fluent natural-sounding English sentences. The input is a string of key-value pairs; the output should be a natural and grammatical English sentence containing all the information from the input.

Positive Example 1 -
Input: name[Aromi], eatType[restaurant], food[English], area[city centre]
Output: Aromi is an English restaurant in the city centre.

Positive Example 2 -
Input: name[The Rice Boat], food[Indian], priceRange[cheap], customer rating[5 out of 5], area[city centre], familyFriendly[yes], near[Express by Holiday Inn]
Output: The Rice Boat is a cheap Indian restaurant in the center of the city near Express by Holiday Inn. It is family friendly and has the highest customer rating.

Negative Example 1 -
Input: name[Blue Spice], eatType[coffee shop], priceRange[more than £30], customer rating[5 out of 5], area[riverside], familyFriendly[yes], near[Avalon]
Output: Blue Spice is a Colombian coffee shop located by the riverside, near Avalon in Boston. Its prices are over £30. Its customer ratings are 5 out of 5.

Negative Example 2 -
Input: name[The Mill], eatType[restaurant], area[riverside], near[The Rice Boat]
Output: A restaurant The Mill, can find near the riverside next to The Rice Boat.

Now complete the following example -
Input: name[Alimentum], food[Fast food], priceRange[cheap], customer rating[average], area[city centre], near[Yippee Noodle Bar]
Output: 


Output: in the city centre near Yippee Noodle Bar there is a fast food place called Alimentum with a average customer rating that is cheap
Definition: In this task, we ask you convert a data table of restaurant descriptions into fluent natural-sounding English sentences. The input is a string of key-value pairs; the output should be a natural and grammatical English sentence containing all the information from the input.

Positive Example 1 -
Input: name[Aromi], eatType[restaurant], food[English], area[city centre]
Output: Aromi is an English restaurant in the city centre.

Positive Example 2 -
Input: name[The Rice Boat], food[Indian], priceRange[cheap], customer rating[5 out of 5], area[city centre], familyFriendly[yes], near[Express by Holiday Inn]
Output: The Rice Boat is a cheap Indian restaurant in the center of the city near Express by Holiday Inn. It is family friendly and has the highest customer rating.

Negative Example 1 -
Input: name[Blue Spice], eatType[coffee shop], priceRange[more than £30], customer rating[5 out of 5], area[riverside], familyFriendly[yes], near[Avalon]
Output: Blue Spice is a Colombian coffee shop located by the riverside, near Avalon in Boston. Its prices are over £30. Its customer ratings are 5 out of 5.

Negative Example 2 -
Input: name[The Mill], eatType[restaurant], area[riverside], near[The Rice Boat]
Output: A restaurant The Mill, can find near the riverside next to The Rice Boat.

Now complete the following example -
Input: name[The Wrestlers], eatType[coffee shop], food[Italian], priceRange[high], area[city centre], familyFriendly[yes], near[Raja Indian Cuisine]
Output: 


Output: A high priced Italian restaurant and coffee shop that is children friendly is called The Wrestlers. It is in the city centre near Raja Indian Cuisine.
Definition: In this task, we ask you convert a data table of restaurant descriptions into fluent natural-sounding English sentences. The input is a string of key-value pairs; the output should be a natural and grammatical English sentence containing all the information from the input.

Positive Example 1 -
Input: name[Aromi], eatType[restaurant], food[English], area[city centre]
Output: Aromi is an English restaurant in the city centre.

Positive Example 2 -
Input: name[The Rice Boat], food[Indian], priceRange[cheap], customer rating[5 out of 5], area[city centre], familyFriendly[yes], near[Express by Holiday Inn]
Output: The Rice Boat is a cheap Indian restaurant in the center of the city near Express by Holiday Inn. It is family friendly and has the highest customer rating.

Negative Example 1 -
Input: name[Blue Spice], eatType[coffee shop], priceRange[more than £30], customer rating[5 out of 5], area[riverside], familyFriendly[yes], near[Avalon]
Output: Blue Spice is a Colombian coffee shop located by the riverside, near Avalon in Boston. Its prices are over £30. Its customer ratings are 5 out of 5.

Negative Example 2 -
Input: name[The Mill], eatType[restaurant], area[riverside], near[The Rice Boat]
Output: A restaurant The Mill, can find near the riverside next to The Rice Boat.

Now complete the following example -
Input: name[Fitzbillies], eatType[coffee shop], food[Fast food], priceRange[cheap], customer rating[average], area[riverside], familyFriendly[no]
Output: 


Output: Fitzbillies is a fast food coffee shop for adults. It has a cheap price range and a rating of average. It is in Riverside.