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--- |
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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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widget: |
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- text: fast:fast boot up, great 1080p resolution, expandable (added 4gb additional |
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ram and a 1tb hd) and great value for it's $365+tax price point. |
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- text: thinness:the sleekness and thinness of this laptop is lightweight and easy |
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to carry. |
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- text: read:when what i heave read, the memory is not upgradeable since it's soldered |
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to the board. |
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- text: memory:a good amount of memory. it doesnt need to have a bunch of memory, |
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but a decent amount is perfect! |
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- text: wifi:nevertheless great processor, great graphics, 16 gb memory runs cool |
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in daily use, battery lasts about 6-7 hours using wifi and video. |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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library_name: setfit |
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inference: false |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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--- |
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# SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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_sm |
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- **SetFitABSA Aspect Model:** [najwaa/absa-laptops-aspect](https://huggingface.co/najwaa/absa-laptops-aspect) |
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- **SetFitABSA Polarity Model:** [najwaa/absa-laptops-polarity](https://huggingface.co/najwaa/absa-laptops-polarity) |
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- **Maximum Sequence Length:** 256 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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<!-- - **License:** 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>'lightweight:very lightweight.'</li><li>'carry:this computer is so light weight and easy to carry.'</li><li>"lightweight:it's lightweight, the screen is decently bright, and it'll go for hours without needing a charge"</li></ul> | |
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| no aspect | <ul><li>'computer:this computer is so light weight and easy to carry.'</li><li>'weight:this computer is so light weight and easy to carry.'</li><li>"screen:it's lightweight, the screen is decently bright, and it'll go for hours without needing a charge"</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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"najwaa/absa-laptops-aspect", |
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"najwaa/absa-laptops-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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--> |
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<!-- |
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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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--> |
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<!-- |
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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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--> |
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<!-- |
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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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--> |
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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 | 1 | 15.9156 | 37 | |
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| Label | Training Sample Count | |
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|:----------|:----------------------| |
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| no aspect | 251 | |
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| aspect | 140 | |
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### Training Hyperparameters |
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- batch_size: (128, 128) |
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- num_epochs: (5, 5) |
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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: True |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0015 | 1 | 0.29 | - | |
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| 0.0770 | 50 | 0.2977 | 0.2646 | |
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| 0.1541 | 100 | 0.2622 | 0.2558 | |
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| 0.2311 | 150 | 0.2493 | 0.2482 | |
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| 0.3082 | 200 | 0.2347 | 0.2261 | |
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| 0.3852 | 250 | 0.1396 | 0.1701 | |
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| 0.4622 | 300 | 0.0514 | 0.1434 | |
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| 0.5393 | 350 | 0.0227 | 0.1808 | |
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| 0.6163 | 400 | 0.0161 | 0.1624 | |
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| 0.6934 | 450 | 0.011 | 0.1718 | |
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| 0.7704 | 500 | 0.0101 | 0.1731 | |
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| 0.8475 | 550 | 0.0089 | 0.1433 | |
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| 0.9245 | 600 | 0.0061 | 0.1682 | |
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| 1.0015 | 650 | 0.0086 | 0.1627 | |
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| 1.0786 | 700 | 0.0078 | 0.1767 | |
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| 1.1556 | 750 | 0.0068 | 0.1773 | |
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| 1.2327 | 800 | 0.0065 | 0.1766 | |
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### Framework Versions |
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- Python: 3.11.12 |
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- SetFit: 1.1.2 |
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- Sentence Transformers: 4.1.0 |
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- spaCy: 3.7.5 |
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- Transformers: 4.51.3 |
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- PyTorch: 2.6.0+cu124 |
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- Datasets: 3.6.0 |
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- Tokenizers: 0.21.1 |
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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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