|
--- |
|
language: en |
|
license: apache-2.0 |
|
library_name: setfit |
|
tags: |
|
- setfit |
|
- absa |
|
- sentence-transformers |
|
- text-classification |
|
- generated_from_setfit_trainer |
|
datasets: |
|
- tomaarsen/setfit-absa-semeval-restaurants |
|
metrics: |
|
- accuracy |
|
widget: |
|
- text: (both in quantity AND quality):The Prix Fixe menu is worth every penny and |
|
you get more than enough (both in quantity AND quality). |
|
- text: over 100 different beers to offer thier:The have over 100 different beers |
|
to offer thier guest so that made my husband very happy and the food was delicious, |
|
if I must recommend a dish it must be the pumkin tortelini. |
|
- text: back with a plate of dumplings.:Get your food to go, find a bench, and kick |
|
back with a plate of dumplings. |
|
- text: the udon was soy sauce and water.:The soup for the udon was soy sauce and |
|
water. |
|
- text: times for the beef cubes - they're:i've been back to nha trang literally a |
|
hundred times for the beef cubes - they're that good. |
|
pipeline_tag: text-classification |
|
inference: false |
|
co2_eq_emissions: |
|
emissions: 15.732253126728272 |
|
source: codecarbon |
|
training_type: fine-tuning |
|
on_cloud: false |
|
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K |
|
ram_total_size: 31.777088165283203 |
|
hours_used: 0.174 |
|
hardware_used: 1 x NVIDIA GeForce RTX 3090 |
|
base_model: BAAI/bge-small-en-v1.5 |
|
model-index: |
|
- name: SetFit Polarity Model with BAAI/bge-small-en-v1.5 on SemEval 2014 Task 4 (Restaurants) |
|
results: |
|
- task: |
|
type: text-classification |
|
name: Text Classification |
|
dataset: |
|
name: SemEval 2014 Task 4 (Restaurants) |
|
type: tomaarsen/setfit-absa-semeval-restaurants |
|
split: test |
|
metrics: |
|
- type: accuracy |
|
value: 0.748561042108452 |
|
name: Accuracy |
|
--- |
|
|
|
# SetFit Polarity Model with BAAI/bge-small-en-v1.5 on SemEval 2014 Task 4 (Restaurants) |
|
|
|
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [SemEval 2014 Task 4 (Restaurants)](https://huggingface.co/datasets/tomaarsen/setfit-absa-semeval-restaurants) dataset that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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. |
|
|
|
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 a SetFit model to filter these possible aspect span candidates. |
|
3. **Use this SetFit model to classify the filtered aspect span candidates.** |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** SetFit |
|
- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |
|
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
|
- **spaCy Model:** en_core_web_lg |
|
- **SetFitABSA Aspect Model:** [tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-aspect](https://huggingface.co/tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-aspect) |
|
- **SetFitABSA Polarity Model:** [tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-polarity](https://huggingface.co/tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-polarity) |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Number of Classes:** 4 classes |
|
- **Training Dataset:** [SemEval 2014 Task 4 (Restaurants)](https://huggingface.co/datasets/tomaarsen/setfit-absa-semeval-restaurants) |
|
- **Language:** en |
|
- **License:** apache-2.0 |
|
|
|
### 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 | |
|
|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| negative | <ul><li>'But the staff was so horrible:But the staff was so horrible to us.'</li><li>', forgot our toast, left out:They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.'</li><li>'did not have mayonnaise, forgot our:They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.'</li></ul> | |
|
| positive | <ul><li>"factor was the food, which was:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"The food is uniformly exceptional:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."</li><li>"a very capable kitchen which will proudly:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."</li></ul> | |
|
| neutral | <ul><li>"'s on the menu or not.:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."</li><li>'to sample both meats).:Our agreed favorite is the orrechiete with sausage and chicken (usually the waiters are kind enough to split the dish in half so you get to sample both meats).'</li><li>'to split the dish in half so:Our agreed favorite is the orrechiete with sausage and chicken (usually the waiters are kind enough to split the dish in half so you get to sample both meats).'</li></ul> | |
|
| conflict | <ul><li>'The food was delicious but:The food was delicious but do not come here on a empty stomach.'</li><li>"The service varys from day:The service varys from day to day- sometimes they're very nice, and sometimes not."</li><li>'Though the Spider Roll may look like:Though the Spider Roll may look like a challenge to eat, with soft shell crab hanging out of the roll, it is well worth the price you pay for them.'</li></ul> | |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
| Label | Accuracy | |
|
|:--------|:---------| |
|
| **all** | 0.7486 | |
|
|
|
## 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( |
|
"tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-aspect", |
|
"tomaarsen/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 | 6 | 22.4980 | 51 | |
|
|
|
| Label | Training Sample Count | |
|
|:---------|:----------------------| |
|
| conflict | 6 | |
|
| negative | 43 | |
|
| neutral | 36 | |
|
| positive | 170 | |
|
|
|
### Training Hyperparameters |
|
- batch_size: (256, 256) |
|
- num_epochs: (5, 5) |
|
- max_steps: 5000 |
|
- 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 | |
|
|:----------:|:-------:|:-------------:|:---------------:| |
|
| 0.0078 | 1 | 0.2397 | - | |
|
| 0.3876 | 50 | 0.2252 | - | |
|
| 0.7752 | 100 | 0.1896 | 0.1883 | |
|
| 1.1628 | 150 | 0.0964 | - | |
|
| **1.5504** | **200** | **0.0307** | **0.1792** | |
|
| 1.9380 | 250 | 0.0275 | - | |
|
| 2.3256 | 300 | 0.0138 | 0.2036 | |
|
| 2.7132 | 350 | 0.006 | - | |
|
| 3.1008 | 400 | 0.0035 | 0.2287 | |
|
| 3.4884 | 450 | 0.0015 | - | |
|
| 3.8760 | 500 | 0.0016 | 0.2397 | |
|
| 4.2636 | 550 | 0.001 | - | |
|
| 4.6512 | 600 | 0.0009 | 0.2477 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
### Environmental Impact |
|
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
|
- **Carbon Emitted**: 0.016 kg of CO2 |
|
- **Hours Used**: 0.174 hours |
|
|
|
### Training Hardware |
|
- **On Cloud**: No |
|
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090 |
|
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K |
|
- **RAM Size**: 31.78 GB |
|
|
|
### Framework Versions |
|
- Python: 3.9.16 |
|
- SetFit: 1.0.0.dev0 |
|
- Sentence Transformers: 2.2.2 |
|
- spaCy: 3.7.2 |
|
- Transformers: 4.29.0 |
|
- PyTorch: 1.13.1+cu117 |
|
- Datasets: 2.15.0 |
|
- Tokenizers: 0.13.3 |
|
|
|
## 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.* |
|
--> |
