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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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metrics: |
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- accuracy |
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widget: |
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- text: bar:After really enjoying ourselves at the bar we sat down at a table and |
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had dinner. |
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- text: interior decor:this little place has a cute interior decor and affordable |
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city prices. |
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- text: cuisine:The cuisine from what I've gathered is authentic Taiwanese, though |
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its very different from what I've been accustomed to in Taipei. |
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- text: dining:Go here for a romantic dinner but not for an all out wow dining experience. |
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- text: Taipei:The cuisine from what I've gathered is authentic Taiwanese, though |
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its very different from what I've been accustomed to in Taipei. |
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pipeline_tag: text-classification |
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inference: false |
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co2_eq_emissions: |
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emissions: 8.62132655272333 |
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source: codecarbon |
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training_type: fine-tuning |
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on_cloud: false |
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cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K |
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ram_total_size: 31.777088165283203 |
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hours_used: 0.111 |
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hardware_used: 1 x NVIDIA GeForce RTX 3090 |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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model-index: |
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- name: SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2 |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.8779507785032646 |
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name: Accuracy |
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--- |
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# SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2 |
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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 [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) 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 filtering aspect span candidates. |
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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 this SetFit model to filter these possible aspect span candidates.** |
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3. Use a 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:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
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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:** en_core_web_lg |
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- **SetFitABSA Aspect Model:** [tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect](https://huggingface.co/tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect) |
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- **SetFitABSA Polarity Model:** [tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity](https://huggingface.co/tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity) |
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- **Maximum Sequence Length:** 512 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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| aspect | <ul><li>'staff:But the staff was so horrible to us.'</li><li>"food: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."</li><li>"food: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."</li></ul> | |
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| no aspect | <ul><li>"factor: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."</li><li>"deficiencies: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."</li><li>"Teodora: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."</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.8780 | |
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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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"tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect", |
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"tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-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 | 4 | 17.9296 | 37 | |
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| Label | Training Sample Count | |
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|:----------|:----------------------| |
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| no aspect | 71 | |
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| aspect | 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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| 0.0007 | 1 | 0.3388 | - | |
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| 0.0370 | 50 | 0.2649 | - | |
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| 0.0740 | 100 | 0.1562 | - | |
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| 0.1109 | 150 | 0.1072 | - | |
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| 0.1479 | 200 | 0.0021 | - | |
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| 0.1849 | 250 | 0.0007 | - | |
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| 0.2219 | 300 | 0.0008 | - | |
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| 0.2589 | 350 | 0.0003 | - | |
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| 0.2959 | 400 | 0.0002 | - | |
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| 0.3328 | 450 | 0.0003 | - | |
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| 0.3698 | 500 | 0.0002 | - | |
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| 0.4068 | 550 | 0.0001 | - | |
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| 0.4438 | 600 | 0.0001 | - | |
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| 0.4808 | 650 | 0.0001 | - | |
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| 0.5178 | 700 | 0.0001 | - | |
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| 0.5547 | 750 | 0.0001 | - | |
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| 0.5917 | 800 | 0.0001 | - | |
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| 0.6287 | 850 | 0.0002 | - | |
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| 0.6657 | 900 | 0.0001 | - | |
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| 0.7027 | 950 | 0.0001 | - | |
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| 0.7396 | 1000 | 0.0001 | - | |
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| 0.7766 | 1050 | 0.0001 | - | |
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| 0.8136 | 1100 | 0.0001 | - | |
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| 0.8506 | 1150 | 0.0001 | - | |
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| 0.8876 | 1200 | 0.0001 | - | |
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| 0.9246 | 1250 | 0.0001 | - | |
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| 0.9615 | 1300 | 0.0001 | - | |
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| 0.9985 | 1350 | 0.0 | - | |
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### Environmental Impact |
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Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
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- **Carbon Emitted**: 0.009 kg of CO2 |
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- **Hours Used**: 0.111 hours |
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### Training Hardware |
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- **On Cloud**: No |
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- **GPU Model**: 1 x NVIDIA GeForce RTX 3090 |
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- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K |
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- **RAM Size**: 31.78 GB |
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### Framework Versions |
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- Python: 3.9.16 |
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- SetFit: 1.0.0.dev0 |
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- Sentence Transformers: 2.2.2 |
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- spaCy: 3.7.2 |
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- Transformers: 4.29.0 |
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- PyTorch: 1.13.1+cu117 |
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- Datasets: 2.15.0 |
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- Tokenizers: 0.13.3 |
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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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