|
--- |
|
library_name: setfit |
|
tags: |
|
- setfit |
|
- absa |
|
- sentence-transformers |
|
- text-classification |
|
- generated_from_setfit_trainer |
|
metrics: |
|
- accuracy |
|
widget: |
|
- text: 'Wifi:Go get your coffee on. |
|
|
|
Hey the coffee was strong, so what else? |
|
|
|
Easy FWY on/off access |
|
|
|
Close the beach |
|
|
|
Other food choice nearby |
|
|
|
Smaller starbucks, with less seating both indoors and outdoors |
|
|
|
Wifi was slow at the time' |
|
- text: "place:Stopped by after a long day visit to Santa Barbara. There were few\ |
|
\ different places near by but remembering that 'smaller place with limited menu'\ |
|
\ has 70/30 chance of being better than a big place with big menu. It's generally\ |
|
\ a good ratio to keep in mind, and depending on the category of food, like sushi,\ |
|
\ it leans to a higher ratio like 80/20. \n\nOther places may have better seating\ |
|
\ and views, but I believe this place has better food. Their clam chowder was\ |
|
\ the best I've had so far in California (been here only for 5 months, I'm just\ |
|
\ getting started). Their 2 options of fresh oysters were good choices. Their\ |
|
\ lemonade was good and sweet. I just wished their shrimp cocktail came with more\ |
|
\ ...Cocktail. (Easy fix, just ask for more)" |
|
- text: "evening:Three reasons why it gets three stars:\n\n1. The crab cakes were\ |
|
\ good and is a definitely must try!\n2. The shrimp scampi was actually amazing\ |
|
\ in the sauce that it comes with, so that's another must try!\n3. The real reason\ |
|
\ why it is getting three stars is because service is everything in ANY restaurant\ |
|
\ you go to. Service started off great, waitress was attentive, but once we paid\ |
|
\ the bill and left a 20% tip, my guests and I, which was only three of us, stayed\ |
|
\ at the table to finish our drinks and we're looking at funny videos from a trip\ |
|
\ we went to. Point is the waitress rudely told my friend to lower the volume\ |
|
\ on his phone, yet other guests were just as loud and we were sitting OUTSIDE...where\ |
|
\ it is already a loud environment! \n\nI really want to give it 4 stars, but\ |
|
\ if I give 4 stars it changes it to, \"Yay! I'm a fan\", but I am not. The only\ |
|
\ reason why it's not getting 1 star, is because the food was decent, the view\ |
|
\ is nice and also the manager was extremely empathetic to the situation and it\ |
|
\ wasn't her fault at all that her waitress was obviously having an off day. I\ |
|
\ have never met a manager that attentive and she was incredible at handling and\ |
|
\ diffusing the situation. I cannot thank her enough for salvaging the rest of\ |
|
\ our evening for how poor the waitress treated paying customers." |
|
- text: Mediterranean: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: sauce:The chicken made worth the waiting, my mild sauce was awesome, the honey |
|
mustard my favorite |
|
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: 0.9602649006622517 |
|
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 |
|
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### 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 | <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> | |
|
| 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> | |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
| Label | Accuracy | |
|
|:--------|:---------| |
|
| **all** | 0.9603 | |
|
|
|
## 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.") |
|
``` |
|
|
|
<!-- |
|
### Downstream Use |
|
|
|
*List how someone could finetune this model on their own dataset.* |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Set Metrics |
|
| Training set | Min | Median | Max | |
|
|:-------------|:----|:---------|:----| |
|
| Word count | 21 | 200.4733 | 491 | |
|
|
|
| Label | Training Sample Count | |
|
|:----------|:----------------------| |
|
| no aspect | 411 | |
|
| aspect | 20 | |
|
|
|
### 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 | 0.2706 | - | |
|
| 0.0147 | 50 | 0.2856 | 0.3049 | |
|
| 0.0294 | 100 | 0.2817 | 0.2904 | |
|
| 0.0442 | 150 | 0.2453 | 0.2837 | |
|
| 0.0589 | 200 | 0.2637 | 0.2756 | |
|
| 0.0736 | 250 | 0.199 | 0.2668 | |
|
| 0.0883 | 300 | 0.1917 | 0.2523 | |
|
| 0.1031 | 350 | 0.1071 | 0.1889 | |
|
| 0.1178 | 400 | 0.049 | 0.0826 | |
|
| **0.1325** | **450** | **0.022** | **0.0718** | |
|
| 0.1472 | 500 | 0.0275 | 0.0767 | |
|
| 0.1620 | 550 | 0.0152 | 0.0779 | |
|
| 0.1767 | 600 | 0.0185 | 0.0905 | |
|
| 0.1914 | 650 | 0.0044 | 0.0785 | |
|
| 0.2061 | 700 | 0.008 | 0.0896 | |
|
|
|
* 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: 4.39.1 |
|
- 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
