--- library_name: setfit tags: - setfit - absa - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: variety:I'm not sure what what I would do if I'd never discovered Nikka, since it's the definitely the most authentic ramen one can get in the area. Prices are standard for ramen (especially in SB) and the service is friendly and efficient. Not only is Nikka's ramen amazing, their variety of appetizers is also great. I've yet to try one that I don't like. Definitely come here if you're looking to satisfy your ramen craving! - text: wrap:Pretty good food, just had a wrap and it was delicious pretty much on Mediterranean or Greek style food around here. Petra's who had really good Greek dinners closed - text: goat cheese:I had the Genoa Salami, Kalamata olive tapenade, with roasted red peppers and goat cheese. I ended up going with this sandwich mainly because I am lactose sensitive and found out that goat cheese is supposed to have less lactose making it easier for the stomach to digest. The goat cheese had a nice smooth and creamy flavor and when combined with the olive tapenade really made a burst of flavor in my mouth. I also had the sandwich on Foccaica bread but they also have three other choices of bread to choose from. Overall the sandwich was delicious. I love the simple clean look of the store and it had some inside seating as well as gated outdoor seating. All the staff members seemed very nice and helpful. The one problem I had with Panino is the price. Although I love the sandwich, I do not believe it is worth $12. When I originally looked up the menu on Yelp I was looking at the pictures that were paired by other reviewers and I saw that they were about $10. $10 still expensive but a little more understandable and worth what you're getting. - text: toppings:FINALLY tried Mizza and wasn't disappointed. Loved (almost) everything we ordered, great atmosphere, excellent service, and the perfect setting for a lovely bday Sunday. The burrata & heirloom tomatoes app was scrumptious, the salmon pasta, very flavorful and the salmon perfectly cooked, I liked the toppings of the veggie pizza but wasn't a super fan of the crust (doesn't mean I won't come back and try another pizza on their menu ) and the cannoli was good although that dessert in general isn't my fave (it was my bf's bday so had to get what he wanted ). The flourless chocolate cake and limoncello cake are what I'll try next time. Had a great time and will be back. Gave it 4 stars just cuz I wasn't that excited about the pizza and that's something they're supposed to so well. Would recommend the restaurant though! - text: mouth:I had the Genoa Salami, Kalamata olive tapenade, with roasted red peppers and goat cheese. I ended up going with this sandwich mainly because I am lactose sensitive and found out that goat cheese is supposed to have less lactose making it easier for the stomach to digest. The goat cheese had a nice smooth and creamy flavor and when combined with the olive tapenade really made a burst of flavor in my mouth. I also had the sandwich on Foccaica bread but they also have three other choices of bread to choose from. Overall the sandwich was delicious. I love the simple clean look of the store and it had some inside seating as well as gated outdoor seating. All the staff members seemed very nice and helpful. The one problem I had with Panino is the price. Although I love the sandwich, I do not believe it is worth $12. When I originally looked up the menu on Yelp I was looking at the pictures that were paired by other reviewers and I saw that they were about $10. $10 still expensive but a little more understandable and worth what you're getting. pipeline_tag: text-classification inference: false base_model: sentence-transformers/all-MiniLM-L6-v2 model-index: - name: SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.956989247311828 name: Accuracy --- # SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2 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. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. This model was trained within the context of a larger system for ABSA, which looks like so: 1. Use a spaCy model to select possible aspect span candidates. 2. **Use this SetFit model to filter these possible aspect span candidates.** 3. Use a SetFit model to classify the filtered aspect span candidates. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **spaCy Model:** en_core_web_sm - **SetFitABSA Aspect Model:** [ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-aspect](https://huggingface.co/ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-aspect) - **SetFitABSA Polarity Model:** [ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-polarity](https://huggingface.co/ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-polarity) - **Maximum Sequence Length:** 256 tokens - **Number of Classes:** 2 classes ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:----------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | aspect | | | no aspect | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.9570 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import AbsaModel # Download from the 🤗 Hub model = AbsaModel.from_pretrained( "ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-aspect", "ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-polarity", ) # Run inference preds = model("The food was great, but the venue is just way too busy.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:---------|:----| | Word count | 21 | 152.7030 | 268 | | Label | Training Sample Count | |:----------|:----------------------| | no aspect | 383 | | aspect | 21 | ### Training Hyperparameters - batch_size: (50, 50) - num_epochs: (5, 5) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: True - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:----------:|:-------:|:-------------:|:---------------:| | 0.0003 | 1 | 0.2856 | - | | 0.0169 | 50 | 0.2755 | 0.3092 | | 0.0339 | 100 | 0.2895 | 0.2962 | | 0.0508 | 150 | 0.2845 | 0.2876 | | 0.0678 | 200 | 0.2471 | 0.2826 | | 0.0847 | 250 | 0.2124 | 0.2691 | | 0.1017 | 300 | 0.1357 | 0.184 | | 0.1186 | 350 | 0.0362 | 0.0871 | | **0.1355** | **400** | **0.07** | **0.0848** | | 0.1525 | 450 | 0.0184 | 0.092 | | 0.1694 | 500 | 0.0179 | 0.096 | | 0.1864 | 550 | 0.0033 | 0.097 | | 0.2033 | 600 | 0.0037 | 0.0978 | | 0.2203 | 650 | 0.04 | 0.1046 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 2.6.0 - spaCy: 3.7.4 - Transformers: 4.38.2 - PyTorch: 2.2.1+cu121 - Datasets: 2.18.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```