---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: span
    dtype: string
  - name: label
    dtype: string
  - name: ordinal
    dtype: int64
  splits:
  - name: train
    num_bytes: 490223
    num_examples: 3693
  - name: test
    num_bytes: 138187
    num_examples: 1134
  download_size: 193352
  dataset_size: 628410
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# 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](https://github.com/huggingface/setfit) ABSA.

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

### Data Instances

An example of "train" looks as follows.

```json
{"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:

```bash
pip install setfit
```

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

```python
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:
```python
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

```bibtex
@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",
}
```