|
--- |
|
library_name: setfit |
|
tags: |
|
- setfit |
|
- absa |
|
- sentence-transformers |
|
- text-classification |
|
- generated_from_setfit_trainer |
|
base_model: sentence-transformers/bert-base-nli-mean-tokens |
|
metrics: |
|
- accuracy |
|
widget: |
|
- text: gamenya seru bagus paket:gamenya seru bagus paket worth it gak lag mudah mainnya |
|
tugas hadiah bagus modenya sayangnya game kadang ngebug gapapa kasih |
|
- text: tolong perbaiki analog nya pengaturan posisi:tolong perbaiki analog nya pengaturan |
|
posisi berpindah pindah |
|
- text: visualisasi bagus segi graphic:visualisasi bagus segi graphic bagus ya game |
|
cocok sih mantra nya banyakin contoh mantra penghilang |
|
- text: jaringan udah bagus game jaringan nya bagus:game nya udah bagus jaringan game |
|
nya bermasalah jaringan udah bagus game jaringan nya bagus mohon nambahin karakter |
|
- text: kali game stuk loading server pakai jaringan:game bagus cma kendala kali game |
|
stuk loading server pakai jaringan wifi masuk jaringan jaringan bermasalah main |
|
game online lancar game susah akses tolong diperbaiki supercell detik bermain |
|
coc lancar masuk kendala |
|
pipeline_tag: text-classification |
|
inference: false |
|
model-index: |
|
- name: SetFit Polarity Model with sentence-transformers/bert-base-nli-mean-tokens |
|
results: |
|
- task: |
|
type: text-classification |
|
name: Text Classification |
|
dataset: |
|
name: Unknown |
|
type: unknown |
|
split: test |
|
metrics: |
|
- type: accuracy |
|
value: 0.8478260869565217 |
|
name: Accuracy |
|
--- |
|
|
|
# SetFit Polarity Model with sentence-transformers/bert-base-nli-mean-tokens |
|
|
|
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/bert-base-nli-mean-tokens](https://huggingface.co/sentence-transformers/bert-base-nli-mean-tokens) 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:** [sentence-transformers/bert-base-nli-mean-tokens](https://huggingface.co/sentence-transformers/bert-base-nli-mean-tokens) |
|
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
|
- **spaCy Model:** id_core_news_trf |
|
- **SetFitABSA Aspect Model:** [Funnyworld1412/ABSA_bert-base_MiniLM-L6-aspect](https://huggingface.co/Funnyworld1412/ABSA_bert-base_MiniLM-L6-aspect) |
|
- **SetFitABSA Polarity Model:** [Funnyworld1412/ABSA_bert-base_MiniLM-L6-polarity](https://huggingface.co/Funnyworld1412/ABSA_bert-base_MiniLM-L6-polarity) |
|
- **Maximum Sequence Length:** 128 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 | |
|
|:--------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| negatif | <ul><li>'seru tolong diperbaiki pencarian lawan bermain ketemu player:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'bugnya nakal banget y:bugnya nakal banget y coc cr aja sukanya ngebug pas match suka hitam match relog kalo udah relog lawan udah 1 2 mahkota kecewa sih bintang nya 1 aja bug nya diurus bintang lawannya kadang g setara levelnya dahlah gk suka banget kalo main 2 vs 2 temen suka banget afk coba fitur report'</li><li>'kadang g setara levelnya dahlah gk suka:bugnya nakal banget y coc cr aja sukanya ngebug pas match suka hitam match relog kalo udah relog lawan udah 1 2 mahkota kecewa sih bintang nya 1 aja bug nya diurus bintang lawannya kadang g setara levelnya dahlah gk suka banget kalo main 2 vs 2 temen suka banget afk coba fitur report'</li></ul> | |
|
| positif | <ul><li>'kapada supercell game nya bagus seru:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'fairrrr mending uninstall gamenya maen game yg:overall gamenya bagus pencarian match dikasih musuh yg levelnya levelku yg pertandingan fair menganggu kenyamanan pemainnya kalo nyariin musuh gapapa nyarinya kasih yg fair levelnya gaush buru buru ngasih yg gak fairrrr pas arena 4 udh dikasih musuh yg pletonnya 2 yg level 11 gak fairrrr mending uninstall gamenya maen game yg yg org gak fairr'</li><li>'gameplay menyenangkan pemain afk:gameplay menyenangkan pemain afk pertengahan menyerah 2vs2 mode mengganggu tolong tambahkan fitur lapor pemain'</li></ul> | |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
| Label | Accuracy | |
|
|:--------|:---------| |
|
| **all** | 0.8478 | |
|
|
|
## 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( |
|
"Funnyworld1412/ABSA_bert-base_MiniLM-L6-aspect", |
|
"Funnyworld1412/ABSA_bert-base_MiniLM-L6-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 | 3 | 28.3626 | 83 | |
|
|
|
| Label | Training Sample Count | |
|
|:--------|:----------------------| |
|
| negatif | 738 | |
|
| positif | 528 | |
|
|
|
### Training Hyperparameters |
|
- batch_size: (4, 4) |
|
- num_epochs: (1, 1) |
|
- max_steps: -1 |
|
- sampling_strategy: oversampling |
|
- num_iterations: 5 |
|
- 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: False |
|
- warmup_proportion: 0.1 |
|
- seed: 42 |
|
- eval_max_steps: -1 |
|
- load_best_model_at_end: False |
|
|
|
### Training Results |
|
| Epoch | Step | Training Loss | Validation Loss | |
|
|:------:|:----:|:-------------:|:---------------:| |
|
| 0.0003 | 1 | 0.3075 | - | |
|
| 0.0158 | 50 | 0.1854 | - | |
|
| 0.0316 | 100 | 0.4431 | - | |
|
| 0.0474 | 150 | 0.3251 | - | |
|
| 0.0632 | 200 | 0.2486 | - | |
|
| 0.0790 | 250 | 0.2371 | - | |
|
| 0.0948 | 300 | 0.3149 | - | |
|
| 0.1106 | 350 | 0.1397 | - | |
|
| 0.1264 | 400 | 0.1131 | - | |
|
| 0.1422 | 450 | 0.2388 | - | |
|
| 0.1580 | 500 | 0.1256 | - | |
|
| 0.1738 | 550 | 0.157 | - | |
|
| 0.1896 | 600 | 0.3768 | - | |
|
| 0.2054 | 650 | 0.022 | - | |
|
| 0.2212 | 700 | 0.221 | - | |
|
| 0.2370 | 750 | 0.122 | - | |
|
| 0.2528 | 800 | 0.028 | - | |
|
| 0.2686 | 850 | 0.102 | - | |
|
| 0.2844 | 900 | 0.2231 | - | |
|
| 0.3002 | 950 | 0.1853 | - | |
|
| 0.3160 | 1000 | 0.2167 | - | |
|
| 0.3318 | 1050 | 0.0054 | - | |
|
| 0.3476 | 1100 | 0.027 | - | |
|
| 0.3633 | 1150 | 0.0189 | - | |
|
| 0.3791 | 1200 | 0.0033 | - | |
|
| 0.3949 | 1250 | 0.2548 | - | |
|
| 0.4107 | 1300 | 0.0043 | - | |
|
| 0.4265 | 1350 | 0.0033 | - | |
|
| 0.4423 | 1400 | 0.0012 | - | |
|
| 0.4581 | 1450 | 0.1973 | - | |
|
| 0.4739 | 1500 | 0.0006 | - | |
|
| 0.4897 | 1550 | 0.001 | - | |
|
| 0.5055 | 1600 | 0.0002 | - | |
|
| 0.5213 | 1650 | 0.2304 | - | |
|
| 0.5371 | 1700 | 0.0005 | - | |
|
| 0.5529 | 1750 | 0.0025 | - | |
|
| 0.5687 | 1800 | 0.0185 | - | |
|
| 0.5845 | 1850 | 0.0023 | - | |
|
| 0.6003 | 1900 | 0.185 | - | |
|
| 0.6161 | 1950 | 0.0004 | - | |
|
| 0.6319 | 2000 | 0.0003 | - | |
|
| 0.6477 | 2050 | 0.0005 | - | |
|
| 0.6635 | 2100 | 0.0126 | - | |
|
| 0.6793 | 2150 | 0.0004 | - | |
|
| 0.6951 | 2200 | 0.0103 | - | |
|
| 0.7109 | 2250 | 0.0009 | - | |
|
| 0.7267 | 2300 | 0.0019 | - | |
|
| 0.7425 | 2350 | 0.0018 | - | |
|
| 0.7583 | 2400 | 0.1837 | - | |
|
| 0.7741 | 2450 | 0.002 | - | |
|
| 0.7899 | 2500 | 0.0003 | - | |
|
| 0.8057 | 2550 | 0.0006 | - | |
|
| 0.8215 | 2600 | 0.2006 | - | |
|
| 0.8373 | 2650 | 0.0003 | - | |
|
| 0.8531 | 2700 | 0.0006 | - | |
|
| 0.8689 | 2750 | 0.0003 | - | |
|
| 0.8847 | 2800 | 0.0001 | - | |
|
| 0.9005 | 2850 | 0.0002 | - | |
|
| 0.9163 | 2900 | 0.0003 | - | |
|
| 0.9321 | 2950 | 0.0002 | - | |
|
| 0.9479 | 3000 | 0.0003 | - | |
|
| 0.9637 | 3050 | 0.001 | - | |
|
| 0.9795 | 3100 | 0.0002 | - | |
|
| 0.9953 | 3150 | 0.0007 | - | |
|
| 1.0 | 3165 | - | 0.2256 | |
|
|
|
### Framework Versions |
|
- Python: 3.10.13 |
|
- SetFit: 1.0.3 |
|
- Sentence Transformers: 3.0.1 |
|
- spaCy: 3.7.5 |
|
- Transformers: 4.36.2 |
|
- PyTorch: 2.1.2 |
|
- Datasets: 2.19.2 |
|
- 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.* |
|
--> |
