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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: louder and the mouse didnt break:I wish the volume could be louder and the |
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mouse didnt break after only a month. |
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- text: + + (sales, service,:BEST BUY - 5 STARS + + + (sales, service, respect for |
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old men who aren't familiar with the technology) DELL COMPUTERS - 3 stars DELL |
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SUPPORT - owes a me a couple |
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- text: back and my built-in webcam and built-:I got it back and my built-in webcam |
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and built-in mic were shorting out anytime I touched the lid, (mind you this was |
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my means of communication with my fiance who was deployed) but I suffered thru |
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it and would constandly have to reset the computer to be able to use my cam and |
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mic anytime they went out. |
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- text: after i install Mozzilla firfox i love every:the only fact i dont like about |
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apples is they generally use safari and i dont use safari but after i install |
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Mozzilla firfox i love every single bit about it. |
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- text: in webcam and built-in mic were shorting out:I got it back and my built-in |
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webcam and built-in mic were shorting out anytime I touched the lid, (mind you |
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this was my means of communication with my fiance who was deployed) but I suffered |
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thru it and would constandly have to reset the computer to be able to use my cam |
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and mic anytime they went out. |
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pipeline_tag: text-classification |
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inference: false |
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base_model: sentence-transformers/all-mpnet-base-v2 |
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model-index: |
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- name: SetFit Polarity Model with sentence-transformers/all-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: tomaarsen/setfit-absa-semeval-laptops |
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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: 0.7007874015748031 |
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name: Accuracy |
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--- |
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# SetFit Polarity Model with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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_sm |
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- **SetFitABSA Aspect Model:** [joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect](https://huggingface.co/joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect) |
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- **SetFitABSA Polarity Model:** [joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity](https://huggingface.co/joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity) |
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- **Maximum Sequence Length:** 384 tokens |
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- **Number of Classes:** 4 classes |
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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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| neutral | <ul><li>'skip taking the cord with me because:I charge it at night and skip taking the cord with me because of the good battery life.'</li><li>'The tech guy then said the: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.'</li><li>'all dark, power light steady, hard:\xa0One night I turned the freaking thing off after using it, the next day I turn it on, no GUI, screen all dark, power light steady, hard drive light steady and not flashing as it usually does.'</li></ul> | |
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| positive | <ul><li>'of the good battery life.:I charge it at night and skip taking the cord with me because of the good battery life.'</li><li>'is of high quality, has a:it is of high quality, has a killer GUI, is extremely stable, is highly expandable, is bundled with lots of very good applications, is easy to use, and is absolutely gorgeous.'</li><li>'has a killer GUI, is extremely:it is of high quality, has a killer GUI, is extremely stable, is highly expandable, is bundled with lots of very good applications, is easy to use, and is absolutely gorgeous.'</li></ul> | |
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| negative | <ul><li>'then said the service center does not do: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.'</li><li>'concern to the "sales" team, which is: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.'</li><li>'on, no GUI, screen all:\xa0One night I turned the freaking thing off after using it, the next day I turn it on, no GUI, screen all dark, power light steady, hard drive light steady and not flashing as it usually does.'</li></ul> | |
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| conflict | <ul><li>'-No backlit keyboard, but not:-No backlit keyboard, but not an issue for me.'</li><li>"to replace the battery once, but:I did have to replace the battery once, but that was only a couple months ago and it's been working perfect ever since."</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** | 0.7008 | |
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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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"joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect", |
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"joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity", |
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spacy_model="en_core_web_sm", |
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) |
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# Run inference |
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preds = model("This laptop meets every expectation and Windows 7 is great!") |
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``` |
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*List how someone could finetune this model on their own dataset.* |
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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 | 25.5873 | 48 | |
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| Label | Training Sample Count | |
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|:---------|:----------------------| |
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| conflict | 2 | |
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| negative | 45 | |
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| neutral | 30 | |
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| positive | 49 | |
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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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- 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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| 0.0120 | 1 | 0.2721 | - | |
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| **0.6024** | **50** | **0.0894** | **0.2059** | |
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| 1.2048 | 100 | 0.0014 | 0.2309 | |
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| 1.8072 | 150 | 0.0006 | 0.2359 | |
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| 2.4096 | 200 | 0.0005 | 0.2373 | |
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| 3.0120 | 250 | 0.0004 | 0.2364 | |
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| 3.6145 | 300 | 0.0003 | 0.2371 | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.11.7 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.3.0 |
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- spaCy: 3.7.2 |
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- Transformers: 4.37.2 |
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- PyTorch: 2.1.2+cu118 |
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- Datasets: 2.16.1 |
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- Tokenizers: 0.15.1 |
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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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