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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: variety:I'm not sure what what I would do if I'd never discovered Nikka, since |
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it's the definitely the most authentic ramen one can get in the area. Prices are |
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standard for ramen (especially in SB) and the service is friendly and efficient. |
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Not only is Nikka's ramen amazing, their variety of appetizers is also great. |
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I've yet to try one that I don't like. Definitely come here if you're looking |
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to satisfy your ramen craving! |
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- text: wrap:Pretty good food, just had a wrap and it was delicious pretty much on |
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Mediterranean or Greek style food around here. Petra's who had really good Greek |
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dinners closed |
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- text: goat cheese:I had the Genoa Salami, Kalamata olive tapenade, with roasted |
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red peppers and goat cheese. I ended up going with this sandwich mainly because |
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I am lactose sensitive and found out that goat cheese is supposed to have less |
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lactose making it easier for the stomach to digest. The goat cheese had a nice |
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smooth and creamy flavor and when combined with the olive tapenade really made |
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a burst of flavor in my mouth. I also had the sandwich on Foccaica bread but they |
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also have three other choices of bread to choose from. Overall the sandwich was |
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delicious. I love the simple clean look of the store and it had some inside seating |
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as well as gated outdoor seating. All the staff members seemed very nice and helpful. |
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The one problem I had with Panino is the price. Although I love the sandwich, |
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I do not believe it is worth $12. When I originally looked up the menu on Yelp |
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I was looking at the pictures that were paired by other reviewers and I saw that |
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they were about $10. $10 still expensive but a little more understandable and |
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worth what you're getting. |
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- text: toppings:FINALLY tried Mizza and wasn't disappointed. Loved (almost) everything |
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we ordered, great atmosphere, excellent service, and the perfect setting for a |
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lovely bday Sunday. The burrata & heirloom tomatoes app was scrumptious, the salmon |
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pasta, very flavorful and the salmon perfectly cooked, I liked the toppings of |
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the veggie pizza but wasn't a super fan of the crust (doesn't mean I won't come |
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back and try another pizza on their menu ) and the cannoli was good although that |
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dessert in general isn't my fave (it was my bf's bday so had to get what he wanted |
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). The flourless chocolate cake and limoncello cake are what I'll try next time. |
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Had a great time and will be back. Gave it 4 stars just cuz I wasn't that excited |
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about the pizza and that's something they're supposed to so well. Would recommend |
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the restaurant though! |
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- text: mouth:I had the Genoa Salami, Kalamata olive tapenade, with roasted red peppers |
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and goat cheese. I ended up going with this sandwich mainly because I am lactose |
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sensitive and found out that goat cheese is supposed to have less lactose making |
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it easier for the stomach to digest. The goat cheese had a nice smooth and creamy |
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flavor and when combined with the olive tapenade really made a burst of flavor |
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in my mouth. I also had the sandwich on Foccaica bread but they also have three |
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other choices of bread to choose from. Overall the sandwich was delicious. I love |
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the simple clean look of the store and it had some inside seating as well as gated |
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outdoor seating. All the staff members seemed very nice and helpful. The one problem |
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I had with Panino is the price. Although I love the sandwich, I do not believe |
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it is worth $12. When I originally looked up the menu on Yelp I was looking at |
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the pictures that were paired by other reviewers and I saw that they were about |
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$10. $10 still expensive but a little more understandable and worth what you're |
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getting. |
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pipeline_tag: text-classification |
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inference: false |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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model-index: |
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- name: SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-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: 0.956989247311828 |
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name: Accuracy |
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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:** [ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-aspect](https://huggingface.co/ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-aspect) |
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- **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) |
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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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### 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>'food:They made it into more American food, added burgers and ribs and got rid of the tequila selection. We were so bummed. Used to be one of our favorite places to go for good Mexican food. The owner said the new direction was to appeal to more tourists.'</li><li>"seating:Such a cute little spot for desserts! I'm so glad we had time on our short visit to Santa Barbara to grab a slice of cake from here. My husband and I each got our own to slice to share of course. He said we didn't come all this way just to get one so we chose a slice of the berry cake and chocolate decadence. The berry cake was nice and fluffy without being too sweet. The acidity from the fruits balanced the sweetest of the cake wonderfully. If you're up for something rich then the chocolate decadence will not disappoint. Service was great and seating was comfortable. Order your sweet treats at the counter then a number will be given to you. Pick a table and get ready to enjoy because your sweets will be brought out to your table when ready."</li><li>'food:One brisk Saturday morning after asking workers during a stop for tylenol from the Hotel California Boutique the best breakfast place, they recommended Goat Tree. We crossed the busy street and greeted the hostess. The very kind young lady walked us to our table on the sunny patio. We skimmed the menu and decided on the chicken and waffle and a chocolate croissant. The wait was quite short and we spent it discussing the beautiful surrounding area. Soon, our food was delivered, and let me tell you, it was beautiful. On top of that, it was scrumptious. The fried chicken was perfect and tender. The waffle had the perfect balance of crunch and fluff. And how dare I forget the exquisite honey. Now this honey was the best I have ever tasted. It was topped with chia and pumpkin seeds. My daughter asked for her croissant warmed, and once again it was marvelous. After paying, I told our waitress how amazing the honey was. Next thing we knew, she brought out two large to go cups full of it! \n\nAbsolutely loved this place and everything about it. 100% recommend! I strongly award them 5 stars!'</li></ul> | |
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| no aspect | <ul><li>'burgers:They made it into more American food, added burgers and ribs and got rid of the tequila selection. We were so bummed. Used to be one of our favorite places to go for good Mexican food. The owner said the new direction was to appeal to more tourists.'</li><li>'ribs:They made it into more American food, added burgers and ribs and got rid of the tequila selection. We were so bummed. Used to be one of our favorite places to go for good Mexican food. The owner said the new direction was to appeal to more tourists.'</li><li>'tequila selection:They made it into more American food, added burgers and ribs and got rid of the tequila selection. We were so bummed. Used to be one of our favorite places to go for good Mexican food. The owner said the new direction was to appeal to more tourists.'</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** | 0.9570 | |
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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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"ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-aspect", |
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"ginkgogo/setfit-absa-bge-small-en-v1.5-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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### 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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*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 | 21 | 152.7030 | 268 | |
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| Label | Training Sample Count | |
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|:----------|:----------------------| |
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| no aspect | 383 | |
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| aspect | 21 | |
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### Training Hyperparameters |
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- batch_size: (50, 50) |
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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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| 0.0003 | 1 | 0.2856 | - | |
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| 0.0169 | 50 | 0.2755 | 0.3092 | |
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| 0.0339 | 100 | 0.2895 | 0.2962 | |
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| 0.0508 | 150 | 0.2845 | 0.2876 | |
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| 0.0678 | 200 | 0.2471 | 0.2826 | |
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| 0.0847 | 250 | 0.2124 | 0.2691 | |
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| 0.1017 | 300 | 0.1357 | 0.184 | |
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| 0.1186 | 350 | 0.0362 | 0.0871 | |
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| **0.1355** | **400** | **0.07** | **0.0848** | |
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| 0.1525 | 450 | 0.0184 | 0.092 | |
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| 0.1694 | 500 | 0.0179 | 0.096 | |
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| 0.1864 | 550 | 0.0033 | 0.097 | |
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| 0.2033 | 600 | 0.0037 | 0.0978 | |
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| 0.2203 | 650 | 0.04 | 0.1046 | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.6.0 |
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- spaCy: 3.7.4 |
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- Transformers: 4.38.2 |
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- PyTorch: 2.2.1+cu121 |
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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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