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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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base_model: firqaaa/indo-setfit-absa-bert-base-restaurants-aspect |
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metrics: |
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- accuracy |
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widget: |
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- text: hp:game yg grafiknya standar boros batrai bikin hp cepat panas game satunya |
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brawlstar ga |
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- text: game:game cocok indonesia gw main game dibilang berat squad buster jaringan |
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game berat bagus squad buster main koneksi terputus koneksi aman aman aja mohon |
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perbaiki jaringan |
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- text: sinyal:prmainannya bagus sinyal diperbaiki maen game online gak bagus2 aja |
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pingnya eh maen squad busters jaringannya hilang2 pas match klok sinyal udah hilang |
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masuk tulisan server konek muat ulang gak masuk in game saran tolong diperbaiki |
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ya min klok grafik gameplay udah bagus |
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- text: saran semoga game:gamenya bagus kendala game nya kadang kadang suka jaringan |
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jaringan bagus saran semoga game nya ditingkatkan disaat update |
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- text: gameplay:gameplay nya bagus gk match nya optimal main kadang suka lag gitu |
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sinyal nya bagus tolong supercell perbaiki sinyal |
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pipeline_tag: text-classification |
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inference: false |
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--- |
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# SetFit Aspect Model with firqaaa/indo-setfit-absa-bert-base-restaurants-aspect |
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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 [firqaaa/indo-setfit-absa-bert-base-restaurants-aspect](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-aspect) 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:** [firqaaa/indo-setfit-absa-bert-base-restaurants-aspect](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-aspect) |
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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:** id_core_news_trf |
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- **SetFitABSA Aspect Model:** [Funnyworld1412/ABSA_review_game-aspect](https://huggingface.co/Funnyworld1412/ABSA_review_game-aspect) |
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- **SetFitABSA Polarity Model:** [Funnyworld1412/ABSA_review_game-polarity](https://huggingface.co/Funnyworld1412/ABSA_review_game-polarity) |
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- **Maximum Sequence Length:** 512 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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<!-- - **Language:** Unknown --> |
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<!-- - **License:** 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>'pencarian lawan:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'game: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: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> | |
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| no aspect | <ul><li>'player trophy mahkotanya jaraknya:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'peleton akun perbedaan level:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'y coc cr: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> | |
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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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"Funnyworld1412/ABSA_review_game-aspect", |
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"Funnyworld1412/ABSA_review_game-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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<!-- |
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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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--> |
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<!-- |
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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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--> |
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<!-- |
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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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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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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 | 2 | 29.9357 | 80 | |
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| Label | Training Sample Count | |
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|:----------|:----------------------| |
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| no aspect | 3834 | |
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| aspect | 1266 | |
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### Training Hyperparameters |
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- batch_size: (4, 4) |
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- num_epochs: (1, 1) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 5 |
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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: False |
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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: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:-----:|:-------------:|:---------------:| |
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| 0.0001 | 1 | 0.0317 | - | |
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| 0.0039 | 50 | 0.0772 | - | |
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| 0.0078 | 100 | 0.2836 | - | |
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| 0.0118 | 150 | 0.2367 | - | |
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| 0.0157 | 200 | 0.1712 | - | |
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| 0.0196 | 250 | 0.0927 | - | |
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| 0.0235 | 300 | 0.3909 | - | |
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| 0.0275 | 350 | 0.038 | - | |
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| 0.0314 | 400 | 0.2421 | - | |
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| 0.0353 | 450 | 0.3313 | - | |
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| 0.0392 | 500 | 0.457 | - | |
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| 0.0431 | 550 | 0.0046 | - | |
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| 0.0471 | 600 | 0.2057 | - | |
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| 0.0510 | 650 | 0.0399 | - | |
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| 0.0549 | 700 | 0.0293 | - | |
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| 0.0588 | 750 | 0.011 | - | |
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| 0.0627 | 800 | 0.3644 | - | |
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| 0.0667 | 850 | 0.0327 | - | |
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| 0.0706 | 900 | 0.3194 | - | |
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| 0.0745 | 950 | 0.2082 | - | |
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| 0.0784 | 1000 | 0.0113 | - | |
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| 0.0824 | 1050 | 0.3499 | - | |
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| 0.0863 | 1100 | 0.1662 | - | |
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| 0.0902 | 1150 | 0.2291 | - | |
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| 0.0941 | 1200 | 0.4169 | - | |
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| 0.0980 | 1250 | 0.2417 | - | |
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| 0.1020 | 1300 | 0.4988 | - | |
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| 0.1059 | 1350 | 0.3409 | - | |
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| 0.1098 | 1400 | 0.1171 | - | |
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| 0.1137 | 1450 | 0.0225 | - | |
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| 0.1176 | 1500 | 0.1653 | - | |
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| 0.1216 | 1550 | 0.0328 | - | |
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| 0.1255 | 1600 | 0.1238 | - | |
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| 0.1294 | 1650 | 0.0158 | - | |
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| 0.1333 | 1700 | 0.2405 | - | |
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| 0.1373 | 1750 | 0.0734 | - | |
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| 0.1412 | 1800 | 0.2323 | - | |
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| 0.1451 | 1850 | 0.0248 | - | |
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| 0.1490 | 1900 | 0.328 | - | |
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| 0.1529 | 1950 | 0.1495 | - | |
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| 0.1569 | 2000 | 0.4438 | - | |
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| 0.1608 | 2050 | 0.0535 | - | |
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| 0.1647 | 2100 | 0.0733 | - | |
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| 0.1686 | 2150 | 0.016 | - | |
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| 0.1725 | 2200 | 0.0214 | - | |
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| 0.1765 | 2250 | 0.0139 | - | |
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| 0.1804 | 2300 | 0.1737 | - | |
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| 0.1843 | 2350 | 0.0847 | - | |
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| 0.1882 | 2400 | 0.052 | - | |
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| 0.1922 | 2450 | 0.0439 | - | |
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| 0.1961 | 2500 | 0.1388 | - | |
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| 0.2 | 2550 | 0.0166 | - | |
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| 0.2039 | 2600 | 0.1413 | - | |
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| 0.2078 | 2650 | 0.1629 | - | |
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| 0.2118 | 2700 | 0.0029 | - | |
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| 0.2157 | 2750 | 0.0373 | - | |
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| 0.2196 | 2800 | 0.0231 | - | |
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| 0.2235 | 2850 | 0.1305 | - | |
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| 0.2275 | 2900 | 0.0079 | - | |
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| 0.2314 | 2950 | 0.0016 | - | |
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| 0.2353 | 3000 | 0.0872 | - | |
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| 0.2392 | 3050 | 0.04 | - | |
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| 0.2431 | 3100 | 0.0041 | - | |
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| 0.2471 | 3150 | 0.0042 | - | |
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| 0.2510 | 3200 | 0.0063 | - | |
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| 0.2549 | 3250 | 0.0092 | - | |
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| 0.2588 | 3300 | 0.0232 | - | |
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| 0.2627 | 3350 | 0.0065 | - | |
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| 0.2667 | 3400 | 0.0345 | - | |
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| 0.2706 | 3450 | 0.0541 | - | |
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| 0.2745 | 3500 | 0.0042 | - | |
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| 0.2784 | 3550 | 0.0016 | - | |
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| 0.2824 | 3600 | 0.0071 | - | |
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| 0.2863 | 3650 | 0.0152 | - | |
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| 0.2902 | 3700 | 0.2999 | - | |
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| 0.2941 | 3750 | 0.1675 | - | |
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| 0.2980 | 3800 | 0.0029 | - | |
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| 0.3020 | 3850 | 0.0037 | - | |
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| 0.3059 | 3900 | 0.4066 | - | |
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| 0.3098 | 3950 | 0.0038 | - | |
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| 0.3137 | 4000 | 0.002 | - | |
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| 0.3176 | 4050 | 0.4448 | - | |
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| 0.3216 | 4100 | 0.0145 | - | |
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| 0.3255 | 4150 | 0.0069 | - | |
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| 0.3294 | 4200 | 0.2314 | - | |
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| 0.3333 | 4250 | 0.2381 | - | |
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| 0.3373 | 4300 | 0.2505 | - | |
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| 0.3412 | 4350 | 0.0002 | - | |
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| 0.3451 | 4400 | 0.013 | - | |
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| 0.3490 | 4450 | 0.0024 | - | |
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| 0.3529 | 4500 | 0.001 | - | |
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| 0.3569 | 4550 | 0.0029 | - | |
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| 0.3608 | 4600 | 0.2407 | - | |
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| 0.3647 | 4650 | 0.0036 | - | |
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| 0.3686 | 4700 | 0.2237 | - | |
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| 0.3725 | 4750 | 0.0114 | - | |
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| 0.3765 | 4800 | 0.1249 | - | |
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| 0.3804 | 4850 | 0.0002 | - | |
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| 0.3843 | 4900 | 0.0035 | - | |
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| 0.3882 | 4950 | 0.0106 | - | |
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| 0.3922 | 5000 | 0.2193 | - | |
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| 0.3961 | 5050 | 0.031 | - | |
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| 0.4 | 5100 | 0.0789 | - | |
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| 0.4039 | 5150 | 0.0329 | - | |
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| 0.4078 | 5200 | 0.0058 | - | |
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| 0.4118 | 5250 | 0.0092 | - | |
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| 0.4157 | 5300 | 0.0015 | - | |
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| 0.4196 | 5350 | 0.0023 | - | |
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| 0.4235 | 5400 | 0.0876 | - | |
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| 0.4275 | 5450 | 0.3165 | - | |
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| 0.4314 | 5500 | 0.0175 | - | |
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| 0.4353 | 5550 | 0.0075 | - | |
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| 0.4392 | 5600 | 0.006 | - | |
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| 0.4431 | 5650 | 0.2122 | - | |
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| 0.4471 | 5700 | 0.1537 | - | |
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| 0.4510 | 5750 | 0.2612 | - | |
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| 0.4549 | 5800 | 0.2033 | - | |
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| 0.4588 | 5850 | 0.2441 | - | |
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| 0.4627 | 5900 | 0.0115 | - | |
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| 0.4667 | 5950 | 0.0015 | - | |
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| 0.4706 | 6000 | 0.0007 | - | |
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| 0.4745 | 6050 | 0.1293 | - | |
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| 0.4784 | 6100 | 0.2008 | - | |
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| 0.4824 | 6150 | 0.0096 | - | |
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| 0.4863 | 6200 | 0.0884 | - | |
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| 0.4902 | 6250 | 0.0025 | - | |
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| 0.4941 | 6300 | 0.0016 | - | |
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| 0.4980 | 6350 | 0.0006 | - | |
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| 0.5020 | 6400 | 0.2076 | - | |
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| 0.5059 | 6450 | 0.0005 | - | |
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| 0.5098 | 6500 | 0.1255 | - | |
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| 0.5137 | 6550 | 0.0301 | - | |
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| 0.5176 | 6600 | 0.0185 | - | |
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| 0.5216 | 6650 | 0.0008 | - | |
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| 0.5255 | 6700 | 0.2277 | - | |
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| 0.5294 | 6750 | 0.0256 | - | |
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| 0.5333 | 6800 | 0.0016 | - | |
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| 0.5373 | 6850 | 0.0249 | - | |
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| 0.5412 | 6900 | 0.1583 | - | |
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| 0.5451 | 6950 | 0.012 | - | |
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| 0.5490 | 7000 | 0.0009 | - | |
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| 0.5529 | 7050 | 0.0011 | - | |
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| 0.5569 | 7100 | 0.0007 | - | |
|
| 0.5608 | 7150 | 0.2337 | - | |
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| 0.5647 | 7200 | 0.3919 | - | |
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| 0.5686 | 7250 | 0.007 | - | |
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| 0.5725 | 7300 | 0.2353 | - | |
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| 0.5765 | 7350 | 0.0081 | - | |
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| 0.5804 | 7400 | 0.0165 | - | |
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| 0.5843 | 7450 | 0.2165 | - | |
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| 0.5882 | 7500 | 0.0781 | - | |
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| 0.5922 | 7550 | 0.1987 | - | |
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| 0.5961 | 7600 | 0.2214 | - | |
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| 0.6 | 7650 | 0.0001 | - | |
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| 0.6039 | 7700 | 0.2061 | - | |
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| 0.6078 | 7750 | 0.187 | - | |
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| 0.6118 | 7800 | 0.0008 | - | |
|
| 0.6157 | 7850 | 0.0397 | - | |
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| 0.6196 | 7900 | 0.2305 | - | |
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| 0.6235 | 7950 | 0.0026 | - | |
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| 0.6275 | 8000 | 0.2132 | - | |
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| 0.6314 | 8050 | 0.1767 | - | |
|
| 0.6353 | 8100 | 0.2049 | - | |
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| 0.6392 | 8150 | 0.0561 | - | |
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| 0.6431 | 8200 | 0.0003 | - | |
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| 0.6471 | 8250 | 0.342 | - | |
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| 0.6510 | 8300 | 0.0126 | - | |
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| 0.6549 | 8350 | 0.3781 | - | |
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| 0.6588 | 8400 | 0.0002 | - | |
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| 0.6627 | 8450 | 0.0002 | - | |
|
| 0.6667 | 8500 | 0.0034 | - | |
|
| 0.6706 | 8550 | 0.0007 | - | |
|
| 0.6745 | 8600 | 0.0004 | - | |
|
| 0.6784 | 8650 | 0.0004 | - | |
|
| 0.6824 | 8700 | 0.0231 | - | |
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| 0.6863 | 8750 | 0.0004 | - | |
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| 0.6902 | 8800 | 0.1727 | - | |
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| 0.6941 | 8850 | 0.0002 | - | |
|
| 0.6980 | 8900 | 0.0011 | - | |
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| 0.7020 | 8950 | 0.0002 | - | |
|
| 0.7059 | 9000 | 0.0259 | - | |
|
| 0.7098 | 9050 | 0.2027 | - | |
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| 0.7137 | 9100 | 0.0016 | - | |
|
| 0.7176 | 9150 | 0.0001 | - | |
|
| 0.7216 | 9200 | 0.0003 | - | |
|
| 0.7255 | 9250 | 0.0081 | - | |
|
| 0.7294 | 9300 | 0.0195 | - | |
|
| 0.7333 | 9350 | 0.1193 | - | |
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| 0.7373 | 9400 | 0.0006 | - | |
|
| 0.7412 | 9450 | 0.0011 | - | |
|
| 0.7451 | 9500 | 0.002 | - | |
|
| 0.7490 | 9550 | 0.1367 | - | |
|
| 0.7529 | 9600 | 0.0001 | - | |
|
| 0.7569 | 9650 | 0.0 | - | |
|
| 0.7608 | 9700 | 0.0001 | - | |
|
| 0.7647 | 9750 | 0.1551 | - | |
|
| 0.7686 | 9800 | 0.0011 | - | |
|
| 0.7725 | 9850 | 0.001 | - | |
|
| 0.7765 | 9900 | 0.0009 | - | |
|
| 0.7804 | 9950 | 0.0003 | - | |
|
| 0.7843 | 10000 | 0.0 | - | |
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| 0.7882 | 10050 | 0.0007 | - | |
|
| 0.7922 | 10100 | 0.1746 | - | |
|
| 0.7961 | 10150 | 0.0007 | - | |
|
| 0.8 | 10200 | 0.0144 | - | |
|
| 0.8039 | 10250 | 0.184 | - | |
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| 0.8078 | 10300 | 0.0003 | - | |
|
| 0.8118 | 10350 | 0.0454 | - | |
|
| 0.8157 | 10400 | 0.003 | - | |
|
| 0.8196 | 10450 | 0.0001 | - | |
|
| 0.8235 | 10500 | 0.0053 | - | |
|
| 0.8275 | 10550 | 0.0001 | - | |
|
| 0.8314 | 10600 | 0.321 | - | |
|
| 0.8353 | 10650 | 0.1054 | - | |
|
| 0.8392 | 10700 | 0.3902 | - | |
|
| 0.8431 | 10750 | 0.0001 | - | |
|
| 0.8471 | 10800 | 0.0004 | - | |
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| 0.8510 | 10850 | 0.0001 | - | |
|
| 0.8549 | 10900 | 0.0001 | - | |
|
| 0.8588 | 10950 | 0.0007 | - | |
|
| 0.8627 | 11000 | 0.0006 | - | |
|
| 0.8667 | 11050 | 0.1327 | - | |
|
| 0.8706 | 11100 | 0.0649 | - | |
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| 0.8745 | 11150 | 0.0005 | - | |
|
| 0.8784 | 11200 | 0.0415 | - | |
|
| 0.8824 | 11250 | 0.0299 | - | |
|
| 0.8863 | 11300 | 0.0002 | - | |
|
| 0.8902 | 11350 | 0.021 | - | |
|
| 0.8941 | 11400 | 0.0001 | - | |
|
| 0.8980 | 11450 | 0.2569 | - | |
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| 0.9020 | 11500 | 0.001 | - | |
|
| 0.9059 | 11550 | 0.1148 | - | |
|
| 0.9098 | 11600 | 0.2232 | - | |
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| 0.9137 | 11650 | 0.0001 | - | |
|
| 0.9176 | 11700 | 0.001 | - | |
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| 0.9216 | 11750 | 0.0011 | - | |
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| 0.9255 | 11800 | 0.0036 | - | |
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| 0.9294 | 11850 | 0.0003 | - | |
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| 0.9333 | 11900 | 0.0004 | - | |
|
| 0.9373 | 11950 | 0.0003 | - | |
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| 0.9412 | 12000 | 0.0002 | - | |
|
| 0.9451 | 12050 | 0.0001 | - | |
|
| 0.9490 | 12100 | 0.1616 | - | |
|
| 0.9529 | 12150 | 0.0001 | - | |
|
| 0.9569 | 12200 | 0.0004 | - | |
|
| 0.9608 | 12250 | 0.1212 | - | |
|
| 0.9647 | 12300 | 0.0005 | - | |
|
| 0.9686 | 12350 | 0.0001 | - | |
|
| 0.9725 | 12400 | 0.0016 | - | |
|
| 0.9765 | 12450 | 0.0003 | - | |
|
| 0.9804 | 12500 | 0.0002 | - | |
|
| 0.9843 | 12550 | 0.0027 | - | |
|
| 0.9882 | 12600 | 0.0005 | - | |
|
| 0.9922 | 12650 | 0.0015 | - | |
|
| 0.9961 | 12700 | 0.0033 | - | |
|
| 1.0 | 12750 | 0.0004 | 0.1762 | |
|
|
|
### 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 |
|
|
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## 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} |
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} |
|
``` |
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