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--- |
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dataset_info: |
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features: |
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- name: text |
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dtype: string |
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- name: span |
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dtype: string |
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- name: label |
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dtype: string |
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- name: ordinal |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 335243 |
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num_examples: 2358 |
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- name: test |
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num_bytes: 76698 |
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num_examples: 654 |
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download_size: 146971 |
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dataset_size: 411941 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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--- |
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# Dataset Card for "tomaarsen/setfit-absa-semeval-laptops" |
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### Dataset Summary |
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This dataset contains the manually annotated laptop reviews from SemEval-2014 Task 4, in the format as |
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understood by [SetFit](https://github.com/huggingface/setfit) ABSA. |
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For more details, see https://aclanthology.org/S14-2004/ |
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### Data Instances |
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An example of "train" looks as follows. |
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```json |
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{"text": "I charge it at night and skip taking the cord with me because of the good battery life.", "span": "cord", "label": "neutral", "ordinal": 0} |
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{"text": "I charge it at night and skip taking the cord with me because of the good battery life.", "span": "battery life", "label": "positive", "ordinal": 0} |
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{"text": "The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the \"sales\" team, which is the retail shop which I bought my netbook from.", "span": "service center", "label": "negative", "ordinal": 0} |
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{"text": "The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the \"sales\" team, which is the retail shop which I bought my netbook from.", "span": "\"sales\" team", "label": "negative", "ordinal": 0} |
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{"text": "The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the \"sales\" team, which is the retail shop which I bought my netbook from.", "span": "tech guy", "label": "neutral", "ordinal": 0} |
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``` |
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### Data Fields |
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The data fields are the same among all splits. |
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- `text`: a `string` feature. |
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- `span`: a `string` feature showing the aspect span from the text. |
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- `label`: a `string` feature showing the polarity of the aspect span. |
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- `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. |
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### Data Splits |
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| name |train|test| |
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|---------|----:|---:| |
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|tomaarsen/setfit-absa-semeval-laptops|2358|654| |
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### Training ABSA models using SetFit ABSA |
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To train using this dataset, first install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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And then you can use the following script as a guideline of how to train an ABSA model on this dataset: |
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```python |
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from setfit import AbsaModel, AbsaTrainer, TrainingArguments |
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from datasets import load_dataset |
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from transformers import EarlyStoppingCallback |
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# You can initialize a AbsaModel using one or two SentenceTransformer models, or two ABSA models |
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model = AbsaModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") |
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# The training/eval dataset must have `text`, `span`, `polarity`, and `ordinal` columns |
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dataset = load_dataset("tomaarsen/setfit-absa-semeval-laptops") |
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train_dataset = dataset["train"] |
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eval_dataset = dataset["test"] |
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args = TrainingArguments( |
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output_dir="models", |
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use_amp=True, |
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batch_size=256, |
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eval_steps=50, |
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save_steps=50, |
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load_best_model_at_end=True, |
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) |
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trainer = AbsaTrainer( |
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model, |
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args=args, |
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train_dataset=train_dataset, |
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eval_dataset=eval_dataset, |
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callbacks=[EarlyStoppingCallback(early_stopping_patience=5)], |
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) |
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trainer.train() |
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metrics = trainer.evaluate(eval_dataset) |
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print(metrics) |
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trainer.push_to_hub("tomaarsen/setfit-absa-laptops") |
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``` |
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You can then run inference like so: |
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```python |
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from setfit import AbsaModel |
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# Download from Hub and run inference |
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model = AbsaModel.from_pretrained( |
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"tomaarsen/setfit-absa-laptops-aspect", |
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"tomaarsen/setfit-absa-laptops-polarity", |
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) |
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# Run inference |
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preds = model([ |
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"Boots up fast and runs great!", |
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"The screen shows great colors.", |
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]) |
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``` |
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### Citation Information |
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```bibtex |
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@inproceedings{pontiki-etal-2014-semeval, |
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title = "{S}em{E}val-2014 Task 4: Aspect Based Sentiment Analysis", |
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author = "Pontiki, Maria and |
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Galanis, Dimitris and |
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Pavlopoulos, John and |
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Papageorgiou, Harris and |
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Androutsopoulos, Ion and |
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Manandhar, Suresh", |
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editor = "Nakov, Preslav and |
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Zesch, Torsten", |
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booktitle = "Proceedings of the 8th International Workshop on Semantic Evaluation ({S}em{E}val 2014)", |
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month = aug, |
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year = "2014", |
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address = "Dublin, Ireland", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/S14-2004", |
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doi = "10.3115/v1/S14-2004", |
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pages = "27--35", |
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} |
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``` |
