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ordinal
int64
0
4
But the staff was so horrible to us.
staff
negative
0
To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora.
food
positive
0
The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not.
food
positive
0
The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not.
kitchen
positive
0
The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not.
menu
neutral
0
Not only was the food outstanding, but the little 'perks' were great.
food
positive
0
Not only was the food outstanding, but the little 'perks' were great.
perks
positive
0
Our agreed favorite is the orrechiete with sausage and chicken (usually the waiters are kind enough to split the dish in half so you get to sample both meats).
orrechiete with sausage and chicken
positive
0
Our agreed favorite is the orrechiete with sausage and chicken (usually the waiters are kind enough to split the dish in half so you get to sample both meats).
waiters
positive
0
Our agreed favorite is the orrechiete with sausage and chicken (usually the waiters are kind enough to split the dish in half so you get to sample both meats).
meats
neutral
0
Our agreed favorite is the orrechiete with sausage and chicken (usually the waiters are kind enough to split the dish in half so you get to sample both meats).
dish
neutral
0
The Bagels have an outstanding taste with a terrific texture, both chewy yet not gummy.
Bagels
positive
0
Nevertheless the food itself is pretty good.
food
positive
0
They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.
toast
negative
0
They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.
mayonnaise
negative
0
They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.
bacon
negative
0
They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.
cheese
neutral
0
They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.
ingredients
negative
0
They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.
plate
neutral
0
They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.
omelet
neutral
0
It took half an hour to get our check, which was perfect since we could sit, have drinks and talk!
drinks
neutral
0
It took half an hour to get our check, which was perfect since we could sit, have drinks and talk!
check
neutral
0
The design and atmosphere is just as good.
design
positive
0
The design and atmosphere is just as good.
atmosphere
positive
0
He has visited Thailand and is quite expert on the cuisine.
cuisine
positive
0
The pizza is the best if you like thin crusted pizza.
pizza
positive
0
The pizza is the best if you like thin crusted pizza.
thin crusted pizza
neutral
0
All the money went into the interior decoration, none of it went to the chefs.
interior decoration
positive
0
All the money went into the interior decoration, none of it went to the chefs.
chefs
negative
0
The seats are uncomfortable if you are sitting against the wall on wooden benches.
seats
negative
0
I asked for seltzer with lime, no ice.
seltzer with lime
neutral
0
Don't go alone---even two people isn't enough for the whole experience, with pickles and a selection of meats and seafoods.
pickles
positive
0
Don't go alone---even two people isn't enough for the whole experience, with pickles and a selection of meats and seafoods.
selection of meats and seafoods
positive
0
My suggestion is to eat family style because you'll want to try the other dishes.
dishes
neutral
0
My suggestion is to eat family style because you'll want to try the other dishes.
eat family style
positive
0
Best of all is the warm vibe, the owner is super friendly and service is fast.
vibe
positive
0
Best of all is the warm vibe, the owner is super friendly and service is fast.
owner
positive
0
Best of all is the warm vibe, the owner is super friendly and service is fast.
service
positive
0
Faan's got a great concept but a little rough on the delivery.
delivery
negative
0
From the incredible food, to the warm atmosphere, to the friendly service, this downtown neighborhood spot doesn't miss a beat.
food
positive
0
From the incredible food, to the warm atmosphere, to the friendly service, this downtown neighborhood spot doesn't miss a beat.
atmosphere
positive
0
From the incredible food, to the warm atmosphere, to the friendly service, this downtown neighborhood spot doesn't miss a beat.
service
positive
0
Great food at REASONABLE prices, makes for an evening that can't be beat!
food
positive
0
Great food at REASONABLE prices, makes for an evening that can't be beat!
prices
positive
0
this little place has a cute interior decor and affordable city prices.
interior decor
positive
0
this little place has a cute interior decor and affordable city prices.
prices
positive
0
Two words: Free wine.
wine
positive
0
The price is reasonable although the service is poor.
price
positive
0
The price is reasonable although the service is poor.
service
negative
0
The quantity is also very good, you will come out satisfied.
quantity
positive
0
I stumbled upon this second floor walk-up two Fridays ago when I was with two friends in town from L.A. Being serious sushi lovers, we sat at the sushi bar to be closer to the action.
sushi
neutral
0
I stumbled upon this second floor walk-up two Fridays ago when I was with two friends in town from L.A. Being serious sushi lovers, we sat at the sushi bar to be closer to the action.
sushi bar
neutral
0
The fried rice is amazing here.
fried rice
positive
0
Three courses - choices include excellent mussels, puff pastry goat cheese and salad with a delicious dressing, and a hanger steak au poivre that is out of this world.
mussels
positive
0
Three courses - choices include excellent mussels, puff pastry goat cheese and salad with a delicious dressing, and a hanger steak au poivre that is out of this world.
puff pastry goat cheese
positive
0
Three courses - choices include excellent mussels, puff pastry goat cheese and salad with a delicious dressing, and a hanger steak au poivre that is out of this world.
salad with a delicious dressing
positive
0
Three courses - choices include excellent mussels, puff pastry goat cheese and salad with a delicious dressing, and a hanger steak au poivre that is out of this world.
hanger steak au poivre
positive
0
Three courses - choices include excellent mussels, puff pastry goat cheese and salad with a delicious dressing, and a hanger steak au poivre that is out of this world.
courses
neutral
0
it's a perfect place to have a amanzing indian food.
indian food
positive
0
The place is so cool and the service is prompt and curtious.
service
positive
0
The place is so cool and the service is prompt and curtious.
place
positive
0
At the end you're left with a mild broth with noodles that you can slurp out of a cup.
broth with noodles
positive
0
I just wonder how you can have such a delicious meal for such little money.
meal
positive
0
I just wonder how you can have such a delicious meal for such little money.
money
positive
0
The food was delicious but do not come here on a empty stomach.
food
conflict
0
The wine list is excellent.
wine list
positive
0
Ive been to many Thai restaurants in Manhattan before, and Toons is by far the best Thai food Ive had (except for my mom's of course).
Thai food
positive
0
They wouldnt even let me finish my glass of wine before offering another.
glass of wine
neutral
0
Whem asked, we had to ask more detailed questions so that we knew what the specials were.
specials
neutral
0
This is a consistently great place to dine for lunch or dinner.
lunch
neutral
0
This is a consistently great place to dine for lunch or dinner.
dinner
neutral
0
This is a consistently great place to dine for lunch or dinner.
dine
positive
0
Nice atmosphere, the service was very pleasant and the desert was good.
atmosphere
positive
0
Nice atmosphere, the service was very pleasant and the desert was good.
service
positive
0
Nice atmosphere, the service was very pleasant and the desert was good.
desert
positive
0
After really enjoying ourselves at the bar we sat down at a table and had dinner.
bar
positive
0
After really enjoying ourselves at the bar we sat down at a table and had dinner.
table
neutral
0
After really enjoying ourselves at the bar we sat down at a table and had dinner.
dinner
neutral
0
Fabulous service, fantastic food, and a chilled out atmosphere and environment.
service
positive
0
Fabulous service, fantastic food, and a chilled out atmosphere and environment.
food
positive
0
Fabulous service, fantastic food, and a chilled out atmosphere and environment.
atmosphere
positive
0
Fabulous service, fantastic food, and a chilled out atmosphere and environment.
environment
positive
0
Try the lasagnette appetizer.
lasagnette appetizer
positive
0
I liked the beer selection!
beer selection
positive
0
Great food, good size menu, great service and an unpretensious setting.
food
positive
0
Great food, good size menu, great service and an unpretensious setting.
menu
positive
0
Great food, good size menu, great service and an unpretensious setting.
service
positive
0
Great food, good size menu, great service and an unpretensious setting.
setting
positive
0
Go here for a romantic dinner but not for an all out wow dining experience.
dinner
positive
0
Go here for a romantic dinner but not for an all out wow dining experience.
dining
positive
0
I grew up eating Dosa and have yet to find a place in NY to satisfy my taste buds.
Dosa
neutral
0
Wine list selection is good and wine-by-the-glass was generously filled to the top.
Wine list selection
positive
0
Wine list selection is good and wine-by-the-glass was generously filled to the top.
wine-by-the-glass
positive
0
With the great variety on the menu , I eat here often and never get bored .
menu
positive
0
The menu is very limited - i think we counted 4 or 5 entrees.
menu
negative
0
The menu is very limited - i think we counted 4 or 5 entrees.
entrees
negative
0
The menu is limited but almost all of the dishes are excellent.
menu
negative
0
The menu is limited but almost all of the dishes are excellent.
dishes
positive
0
Not too crazy about their sake martini.
sake martini
negative
0
Great bagels, spreads and a good place to hang out in.
bagels
positive
0

Dataset Card for "tomaarsen/setfit-absa-semeval-restaurants"

Dataset Summary

This dataset contains the manually annotated restaurant reviews from SemEval-2014 Task 4, in the format as understood by SetFit ABSA.

For more details, see https://aclanthology.org/S14-2004/

Data Instances

An example of "train" looks as follows.

{"text": "But the staff was so horrible to us.", "span": "staff", "label": "negative", "ordinal": 0}
{"text": "To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora.", "span": "food", "label": "positive", "ordinal": 0}
{"text": "The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not.", "span": "food", "label": "positive", "ordinal": 0}
{"text": "The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not.", "span": "kitchen", "label": "positive", "ordinal": 0}
{"text": "The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not.", "span": "menu", "label": "neutral", "ordinal": 0}

Data Fields

The data fields are the same among all splits.

  • text: a string feature.
  • span: a string feature showing the aspect span from the text.
  • label: a string feature showing the polarity of the aspect span.
  • ordinal: an int64 feature showing the n-th occurrence of the span in the text. This is useful for if the span occurs within the same text multiple times.

Data Splits

name train test
tomaarsen/setfit-absa-semeval-restaurants 3693 1134

Training ABSA models using SetFit ABSA

To train using this dataset, first install the SetFit library:

pip install setfit

And then you can use the following script as a guideline of how to train an ABSA model on this dataset:

from setfit import AbsaModel, AbsaTrainer, TrainingArguments
from datasets import load_dataset
from transformers import EarlyStoppingCallback

# You can initialize a AbsaModel using one or two SentenceTransformer models, or two ABSA models
model = AbsaModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")

# The training/eval dataset must have `text`, `span`, `polarity`, and `ordinal` columns
dataset = load_dataset("tomaarsen/setfit-absa-semeval-restaurants")
train_dataset = dataset["train"]
eval_dataset = dataset["test"]

args = TrainingArguments(
    output_dir="models",
    use_amp=True,
    batch_size=256,
    eval_steps=50,
    save_steps=50,
    load_best_model_at_end=True,
)

trainer = AbsaTrainer(
    model,
    args=args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    callbacks=[EarlyStoppingCallback(early_stopping_patience=5)],
)
trainer.train()

metrics = trainer.evaluate(eval_dataset)
print(metrics)

trainer.push_to_hub("tomaarsen/setfit-absa-restaurants")

You can then run inference like so:

from setfit import AbsaModel

# Download from Hub and run inference
model = AbsaModel.from_pretrained(
    "tomaarsen/setfit-absa-restaurants-aspect",
    "tomaarsen/setfit-absa-restaurants-polarity",
)

# Run inference
preds = model([
    "The best pizza outside of Italy and really tasty.",
    "The food here is great but the service is terrible",
])

Citation Information

@inproceedings{pontiki-etal-2014-semeval,
    title = "{S}em{E}val-2014 Task 4: Aspect Based Sentiment Analysis",
    author = "Pontiki, Maria  and
      Galanis, Dimitris  and
      Pavlopoulos, John  and
      Papageorgiou, Harris  and
      Androutsopoulos, Ion  and
      Manandhar, Suresh",
    editor = "Nakov, Preslav  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 8th International Workshop on Semantic Evaluation ({S}em{E}val 2014)",
    month = aug,
    year = "2014",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S14-2004",
    doi = "10.3115/v1/S14-2004",
    pages = "27--35",
}
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