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--- |
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library_name: setfit |
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tags: |
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- setfit |
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- absa |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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base_model: cointegrated/rubert-tiny2 |
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metrics: |
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- accuracy |
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widget: |
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- text: а л а палтуса запеченного – х о:П о п р о б о в а л а палтуса запеченного |
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– х о р о ш , д а и к р а с и в о с м о т р и т с я н а т а р е л к е . |
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- text: 'с курицей , лосось со шпинатным соусом , чай облепиховый:При каждом новом |
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посещении я стараюсь пробовать новые блюда из меню , особенно мне понравились |
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: цезарь с курицей , лосось со шпинатным соусом , чай облепиховый и тирамису от |
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шеф повара .' |
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- text: ', но качество еды ее не украсило:Свадьба , конечно , прошла весело , но качество |
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еды ее не украсило .' |
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- text: найти уютное недорогое местечко в районе метро:Думаю , если стоит задача найти |
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уютное недорогое местечко в районе метро московская , то это наверно один из лучших |
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вариантов . |
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- text: они начали разнообразить кухню мясными блюдами ,:Хочется , чтобы мой отзыв |
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дошел до администрации , и они начали разнообразить кухню мясными блюдами , гарнирами |
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, интересными салатами и супами . |
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pipeline_tag: text-classification |
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inference: false |
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--- |
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# SetFit Polarity Model with cointegrated/rubert-tiny2 |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of classifying aspect polarities. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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This model was trained within the context of a larger system for ABSA, which looks like so: |
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1. Use a spaCy model to select possible aspect span candidates. |
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2. Use a SetFit model to filter these possible aspect span candidates. |
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3. **Use this SetFit model to classify the filtered aspect span candidates.** |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **spaCy Model:** ru_core_news_lg |
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- **SetFitABSA Aspect Model:** [isolation-forest/setfit-absa-aspect](https://huggingface.co/isolation-forest/setfit-absa-aspect) |
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- **SetFitABSA Polarity Model:** [isolation-forest/setfit-absa-polarity](https://huggingface.co/isolation-forest/setfit-absa-polarity) |
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- **Maximum Sequence Length:** 2048 tokens |
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- **Number of Classes:** 2 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| Positive | <ul><li>'И порции " достойные ":И порции " достойные " .'</li><li>'Салаты вообще оказались вкуснейшими:Салаты вообще оказались вкуснейшими .'</li><li>'порадовала , большая пивная тарелка , действительно оказалась:Кухня порадовала , большая пивная тарелка , действительно оказалась большой и вкусной !'</li></ul> | |
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| Negative | <ul><li>'Потом официантка как будто пропала:Потом официантка как будто пропала , было не дозваться , чтобы что - то дозаказать , очень долго приходилось ждать , в итоге посчитали неправильно , в счет внесли на 2 пункта больше , чем мы заказывали .'</li><li>'Обслуживание не впечатлило .:Обслуживание не впечатлило .'</li><li>'приятно удивлена " китайским интерьером " - диванчики:Была приятно удивлена " китайским интерьером " - диванчики как в бистро , скатерти на столах по типу а - ля столовая , европейские светильники / люстры , в общем в плане интерьера китайского никакого абсолютно !'</li></ul> | |
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## Uses |
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### Direct Use for Inference |
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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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Then you can load this model and run inference. |
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```python |
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from setfit import AbsaModel |
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# Download from the 🤗 Hub |
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model = AbsaModel.from_pretrained( |
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"isolation-forest/setfit-absa-aspect", |
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"isolation-forest/setfit-absa-polarity", |
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) |
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# Run inference |
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preds = model("The food was great, but the venue is just way too busy.") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 3 | 28.4766 | 92 | |
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| Label | Training Sample Count | |
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|:---------|:----------------------| |
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| Negative | 128 | |
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| Positive | 128 | |
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### Training Hyperparameters |
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- batch_size: (16, 2) |
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- num_epochs: (1, 16) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0005 | 1 | 0.2196 | - | |
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| 0.0242 | 50 | 0.2339 | - | |
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| 0.0484 | 100 | 0.2258 | - | |
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| 0.0727 | 150 | 0.246 | - | |
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| 0.0969 | 200 | 0.1963 | - | |
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| 0.1211 | 250 | 0.18 | - | |
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| 0.1453 | 300 | 0.1176 | - | |
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| 0.1696 | 350 | 0.0588 | - | |
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| 0.1938 | 400 | 0.0482 | - | |
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| 0.2180 | 450 | 0.1131 | - | |
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| 0.2422 | 500 | 0.0134 | - | |
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| 0.2665 | 550 | 0.0415 | - | |
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| 0.2907 | 600 | 0.0144 | - | |
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| 0.3149 | 650 | 0.012 | - | |
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| 0.3391 | 700 | 0.0091 | - | |
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| 0.3634 | 750 | 0.0055 | - | |
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| 0.3876 | 800 | 0.0054 | - | |
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| 0.4118 | 850 | 0.0055 | - | |
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| 0.4360 | 900 | 0.0072 | - | |
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| 0.4603 | 950 | 0.0094 | - | |
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| 0.4845 | 1000 | 0.0054 | - | |
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| 0.5087 | 1050 | 0.0045 | - | |
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| 0.5329 | 1100 | 0.003 | - | |
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| 0.5572 | 1150 | 0.0067 | - | |
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| 0.5814 | 1200 | 0.0041 | - | |
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| 0.6056 | 1250 | 0.0048 | - | |
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| 0.6298 | 1300 | 0.0053 | - | |
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| 0.6541 | 1350 | 0.0048 | - | |
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| 0.6783 | 1400 | 0.0038 | - | |
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| 0.7025 | 1450 | 0.0037 | - | |
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| 0.7267 | 1500 | 0.0031 | - | |
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| 0.7510 | 1550 | 0.0038 | - | |
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| 0.7752 | 1600 | 0.0032 | - | |
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| 0.7994 | 1650 | 0.0039 | - | |
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| 0.8236 | 1700 | 0.0032 | - | |
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| 0.8479 | 1750 | 0.0023 | - | |
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| 0.8721 | 1800 | 0.0029 | - | |
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| 0.8963 | 1850 | 0.0041 | - | |
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| 0.9205 | 1900 | 0.0026 | - | |
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| 0.9448 | 1950 | 0.0027 | - | |
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| 0.9690 | 2000 | 0.0035 | - | |
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| 0.9932 | 2050 | 0.003 | - | |
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### Framework Versions |
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- Python: 3.10.13 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.7.0 |
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- spaCy: 3.7.2 |
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- Transformers: 4.39.3 |
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- PyTorch: 2.1.2 |
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- Datasets: 2.18.0 |
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- Tokenizers: 0.15.2 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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