setfit safetensors bert absa sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/all-MiniLM-L6-v2 base_model:finetune:sentence-transformers/all-MiniLM-L6-v2 model-index region:us
---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: hp:game yg grafiknya standar boros batrai bikin hp cepat panas game satunya
    brawlstar ga
- text: game:game cocok indonesia gw main game dibilang berat squad buster jaringan
    game berat bagus squad buster main koneksi terputus koneksi aman aman aja mohon
    perbaiki jaringan
- text: sinyal:prmainannya bagus sinyal diperbaiki maen game online gak bagus2 aja
    pingnya eh maen squad busters jaringannya hilang2 pas match klok sinyal udah hilang
    masuk tulisan server konek muat ulang gak masuk in game saran tolong diperbaiki
    ya min klok grafik gameplay udah bagus
- text: saran semoga game:gamenya bagus kendala game nya kadang kadang suka jaringan
    jaringan bagus saran semoga game nya ditingkatkan disaat update
- text: gameplay:gameplay nya bagus gk match nya optimal main kadang suka lag gitu
    sinyal nya bagus tolong supercell perbaiki sinyal
inference: false
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: 0.8307086614173228
      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:** id_core_news_trf
- **SetFitABSA Aspect Model:** [Funnyworld1412/ABSA_indo-sentence-bert-large_MiniLM-L6-aspect](https://huggingface.co/Funnyworld1412/ABSA_indo-sentence-bert-large_MiniLM-L6-aspect)
- **SetFitABSA Polarity Model:** [Funnyworld1412/ABSA_indo-sentence-bert-large_MiniLM-L6-polarity](https://huggingface.co/Funnyworld1412/ABSA_indo-sentence-bert-large_MiniLM-L6-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>'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>                                             |
| 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> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8307   |

## 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_indo-sentence-bert-large_MiniLM-L6-aspect",
    "Funnyworld1412/ABSA_indo-sentence-bert-large_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   | 2   | 29.9357 | 80  |

| Label     | Training Sample Count |
|:----------|:----------------------|
| no aspect | 3834                  |
| aspect    | 1266                  |

### 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0001 | 1     | 0.2715        | -               |
| 0.0039 | 50    | 0.2364        | -               |
| 0.0078 | 100   | 0.1076        | -               |
| 0.0118 | 150   | 0.3431        | -               |
| 0.0157 | 200   | 0.2411        | -               |
| 0.0196 | 250   | 0.361         | -               |
| 0.0235 | 300   | 0.2227        | -               |
| 0.0275 | 350   | 0.2087        | -               |
| 0.0314 | 400   | 0.1956        | -               |
| 0.0353 | 450   | 0.2815        | -               |
| 0.0392 | 500   | 0.1844        | -               |
| 0.0431 | 550   | 0.2053        | -               |
| 0.0471 | 600   | 0.2884        | -               |
| 0.0510 | 650   | 0.1043        | -               |
| 0.0549 | 700   | 0.2074        | -               |
| 0.0588 | 750   | 0.1627        | -               |
| 0.0627 | 800   | 0.3           | -               |
| 0.0667 | 850   | 0.1658        | -               |
| 0.0706 | 900   | 0.1582        | -               |
| 0.0745 | 950   | 0.2692        | -               |
| 0.0784 | 1000  | 0.1823        | -               |
| 0.0824 | 1050  | 0.4098        | -               |
| 0.0863 | 1100  | 0.1992        | -               |
| 0.0902 | 1150  | 0.0793        | -               |
| 0.0941 | 1200  | 0.3924        | -               |
| 0.0980 | 1250  | 0.0339        | -               |
| 0.1020 | 1300  | 0.2236        | -               |
| 0.1059 | 1350  | 0.2262        | -               |
| 0.1098 | 1400  | 0.111         | -               |
| 0.1137 | 1450  | 0.0223        | -               |
| 0.1176 | 1500  | 0.3994        | -               |
| 0.1216 | 1550  | 0.0417        | -               |
| 0.1255 | 1600  | 0.3319        | -               |
| 0.1294 | 1650  | 0.3223        | -               |
| 0.1333 | 1700  | 0.2943        | -               |
| 0.1373 | 1750  | 0.1273        | -               |
| 0.1412 | 1800  | 0.2863        | -               |
| 0.1451 | 1850  | 0.0988        | -               |
| 0.1490 | 1900  | 0.1593        | -               |
| 0.1529 | 1950  | 0.2209        | -               |
| 0.1569 | 2000  | 0.5017        | -               |
| 0.1608 | 2050  | 0.1392        | -               |
| 0.1647 | 2100  | 0.1372        | -               |
| 0.1686 | 2150  | 0.3491        | -               |
| 0.1725 | 2200  | 0.2693        | -               |
| 0.1765 | 2250  | 0.1988        | -               |
| 0.1804 | 2300  | 0.2765        | -               |
| 0.1843 | 2350  | 0.238         | -               |
| 0.1882 | 2400  | 0.0577        | -               |
| 0.1922 | 2450  | 0.2253        | -               |
| 0.1961 | 2500  | 0.16          | -               |
| 0.2    | 2550  | 0.0262        | -               |
| 0.2039 | 2600  | 0.0099        | -               |
| 0.2078 | 2650  | 0.0132        | -               |
| 0.2118 | 2700  | 0.2356        | -               |
| 0.2157 | 2750  | 0.2975        | -               |
| 0.2196 | 2800  | 0.154         | -               |
| 0.2235 | 2850  | 0.0308        | -               |
| 0.2275 | 2900  | 0.0497        | -               |
| 0.2314 | 2950  | 0.0523        | -               |
| 0.2353 | 3000  | 0.158         | -               |
| 0.2392 | 3050  | 0.0473        | -               |
| 0.2431 | 3100  | 0.208         | -               |
| 0.2471 | 3150  | 0.2126        | -               |
| 0.2510 | 3200  | 0.081         | -               |
| 0.2549 | 3250  | 0.0134        | -               |
| 0.2588 | 3300  | 0.1107        | -               |
| 0.2627 | 3350  | 0.0249        | -               |
| 0.2667 | 3400  | 0.0259        | -               |
| 0.2706 | 3450  | 0.1008        | -               |
| 0.2745 | 3500  | 0.0335        | -               |
| 0.2784 | 3550  | 0.0119        | -               |
| 0.2824 | 3600  | 0.2982        | -               |
| 0.2863 | 3650  | 0.1516        | -               |
| 0.2902 | 3700  | 0.1217        | -               |
| 0.2941 | 3750  | 0.1558        | -               |
| 0.2980 | 3800  | 0.0359        | -               |
| 0.3020 | 3850  | 0.0215        | -               |
| 0.3059 | 3900  | 0.2906        | -               |
| 0.3098 | 3950  | 0.0599        | -               |
| 0.3137 | 4000  | 0.1528        | -               |
| 0.3176 | 4050  | 0.0144        | -               |
| 0.3216 | 4100  | 0.298         | -               |
| 0.3255 | 4150  | 0.0174        | -               |
| 0.3294 | 4200  | 0.0093        | -               |
| 0.3333 | 4250  | 0.0329        | -               |
| 0.3373 | 4300  | 0.1795        | -               |
| 0.3412 | 4350  | 0.0712        | -               |
| 0.3451 | 4400  | 0.3703        | -               |
| 0.3490 | 4450  | 0.0873        | -               |
| 0.3529 | 4500  | 0.3223        | -               |
| 0.3569 | 4550  | 0.0045        | -               |
| 0.3608 | 4600  | 0.2188        | -               |
| 0.3647 | 4650  | 0.0085        | -               |
| 0.3686 | 4700  | 0.2089        | -               |
| 0.3725 | 4750  | 0.0052        | -               |
| 0.3765 | 4800  | 0.1459        | -               |
| 0.3804 | 4850  | 0.0711        | -               |
| 0.3843 | 4900  | 0.4268        | -               |
| 0.3882 | 4950  | 0.1842        | -               |
| 0.3922 | 5000  | 0.1661        | -               |
| 0.3961 | 5050  | 0.1028        | -               |
| 0.4    | 5100  | 0.067         | -               |
| 0.4039 | 5150  | 0.1708        | -               |
| 0.4078 | 5200  | 0.1001        | -               |
| 0.4118 | 5250  | 0.065         | -               |
| 0.4157 | 5300  | 0.0279        | -               |
| 0.4196 | 5350  | 0.1101        | -               |
| 0.4235 | 5400  | 0.1923        | -               |
| 0.4275 | 5450  | 0.5491        | -               |
| 0.4314 | 5500  | 0.0726        | -               |
| 0.4353 | 5550  | 0.0085        | -               |
| 0.4392 | 5600  | 0.194         | -               |
| 0.4431 | 5650  | 0.2527        | -               |
| 0.4471 | 5700  | 0.7134        | -               |
| 0.4510 | 5750  | 0.4542        | -               |
| 0.4549 | 5800  | 0.2779        | -               |
| 0.4588 | 5850  | 0.1024        | -               |
| 0.4627 | 5900  | 0.2483        | -               |
| 0.4667 | 5950  | 0.0163        | -               |
| 0.4706 | 6000  | 0.0095        | -               |
| 0.4745 | 6050  | 0.2902        | -               |
| 0.4784 | 6100  | 0.0111        | -               |
| 0.4824 | 6150  | 0.0296        | -               |
| 0.4863 | 6200  | 0.3792        | -               |
| 0.4902 | 6250  | 0.4387        | -               |
| 0.4941 | 6300  | 0.1547        | -               |
| 0.4980 | 6350  | 0.0617        | -               |
| 0.5020 | 6400  | 0.1384        | -               |
| 0.5059 | 6450  | 0.0677        | -               |
| 0.5098 | 6500  | 0.0454        | -               |
| 0.5137 | 6550  | 0.0074        | -               |
| 0.5176 | 6600  | 0.1994        | -               |
| 0.5216 | 6650  | 0.0168        | -               |
| 0.5255 | 6700  | 0.0416        | -               |
| 0.5294 | 6750  | 0.1898        | -               |
| 0.5333 | 6800  | 0.0207        | -               |
| 0.5373 | 6850  | 0.1046        | -               |
| 0.5412 | 6900  | 0.1994        | -               |
| 0.5451 | 6950  | 0.0435        | -               |
| 0.5490 | 7000  | 0.0149        | -               |
| 0.5529 | 7050  | 0.0067        | -               |
| 0.5569 | 7100  | 0.0122        | -               |
| 0.5608 | 7150  | 0.2406        | -               |
| 0.5647 | 7200  | 0.4473        | -               |
| 0.5686 | 7250  | 0.0469        | -               |
| 0.5725 | 7300  | 0.1782        | -               |
| 0.5765 | 7350  | 0.3386        | -               |
| 0.5804 | 7400  | 0.2804        | -               |
| 0.5843 | 7450  | 0.0072        | -               |
| 0.5882 | 7500  | 0.0451        | -               |
| 0.5922 | 7550  | 0.0188        | -               |
| 0.5961 | 7600  | 0.01          | -               |
| 0.6    | 7650  | 0.0048        | -               |
| 0.6039 | 7700  | 0.2349        | -               |
| 0.6078 | 7750  | 0.2052        | -               |
| 0.6118 | 7800  | 0.0838        | -               |
| 0.6157 | 7850  | 0.3052        | -               |
| 0.6196 | 7900  | 0.3667        | -               |
| 0.6235 | 7950  | 0.0044        | -               |
| 0.6275 | 8000  | 0.3612        | -               |
| 0.6314 | 8050  | 0.2082        | -               |
| 0.6353 | 8100  | 0.3384        | -               |
| 0.6392 | 8150  | 0.022         | -               |
| 0.6431 | 8200  | 0.0764        | -               |
| 0.6471 | 8250  | 0.2879        | -               |
| 0.6510 | 8300  | 0.1827        | -               |
| 0.6549 | 8350  | 0.1104        | -               |
| 0.6588 | 8400  | 0.2096        | -               |
| 0.6627 | 8450  | 0.2103        | -               |
| 0.6667 | 8500  | 0.0742        | -               |
| 0.6706 | 8550  | 0.2186        | -               |
| 0.6745 | 8600  | 0.0109        | -               |
| 0.6784 | 8650  | 0.0326        | -               |
| 0.6824 | 8700  | 0.3056        | -               |
| 0.6863 | 8750  | 0.0941        | -               |
| 0.6902 | 8800  | 0.3731        | -               |
| 0.6941 | 8850  | 0.2185        | -               |
| 0.6980 | 8900  | 0.0228        | -               |
| 0.7020 | 8950  | 0.0141        | -               |
| 0.7059 | 9000  | 0.2242        | -               |
| 0.7098 | 9050  | 0.3303        | -               |
| 0.7137 | 9100  | 0.2383        | -               |
| 0.7176 | 9150  | 0.0026        | -               |
| 0.7216 | 9200  | 0.1718        | -               |
| 0.7255 | 9250  | 0.053         | -               |
| 0.7294 | 9300  | 0.0023        | -               |
| 0.7333 | 9350  | 0.221         | -               |
| 0.7373 | 9400  | 0.0021        | -               |
| 0.7412 | 9450  | 0.2333        | -               |
| 0.7451 | 9500  | 0.0565        | -               |
| 0.7490 | 9550  | 0.0271        | -               |
| 0.7529 | 9600  | 0.2156        | -               |
| 0.7569 | 9650  | 0.2349        | -               |
| 0.7608 | 9700  | 0.0047        | -               |
| 0.7647 | 9750  | 0.1273        | -               |
| 0.7686 | 9800  | 0.0139        | -               |
| 0.7725 | 9850  | 0.0231        | -               |
| 0.7765 | 9900  | 0.0048        | -               |
| 0.7804 | 9950  | 0.0022        | -               |
| 0.7843 | 10000 | 0.0026        | -               |
| 0.7882 | 10050 | 0.0223        | -               |
| 0.7922 | 10100 | 0.5488        | -               |
| 0.7961 | 10150 | 0.0281        | -               |
| 0.8    | 10200 | 0.0999        | -               |
| 0.8039 | 10250 | 0.2154        | -               |
| 0.8078 | 10300 | 0.0109        | -               |
| 0.8118 | 10350 | 0.0019        | -               |
| 0.8157 | 10400 | 0.1264        | -               |
| 0.8196 | 10450 | 0.0029        | -               |
| 0.8235 | 10500 | 0.3785        | -               |
| 0.8275 | 10550 | 0.0366        | -               |
| 0.8314 | 10600 | 0.0527        | -               |
| 0.8353 | 10650 | 0.2355        | -               |
| 0.8392 | 10700 | 0.0833        | -               |
| 0.8431 | 10750 | 0.1612        | -               |
| 0.8471 | 10800 | 0.0071        | -               |
| 0.8510 | 10850 | 0.1128        | -               |
| 0.8549 | 10900 | 0.2521        | -               |
| 0.8588 | 10950 | 0.0403        | -               |
| 0.8627 | 11000 | 0.2196        | -               |
| 0.8667 | 11050 | 0.1441        | -               |
| 0.8706 | 11100 | 0.0295        | -               |
| 0.8745 | 11150 | 0.0047        | -               |
| 0.8784 | 11200 | 0.3089        | -               |
| 0.8824 | 11250 | 0.1055        | -               |
| 0.8863 | 11300 | 0.0064        | -               |
| 0.8902 | 11350 | 0.2119        | -               |
| 0.8941 | 11400 | 0.2145        | -               |
| 0.8980 | 11450 | 0.0128        | -               |
| 0.9020 | 11500 | 0.0086        | -               |
| 0.9059 | 11550 | 0.1803        | -               |
| 0.9098 | 11600 | 0.2277        | -               |
| 0.9137 | 11650 | 0.0204        | -               |
| 0.9176 | 11700 | 0.0105        | -               |
| 0.9216 | 11750 | 0.005         | -               |
| 0.9255 | 11800 | 0.0099        | -               |
| 0.9294 | 11850 | 0.004         | -               |
| 0.9333 | 11900 | 0.1824        | -               |
| 0.9373 | 11950 | 0.0021        | -               |
| 0.9412 | 12000 | 0.2231        | -               |
| 0.9451 | 12050 | 0.0017        | -               |
| 0.9490 | 12100 | 0.0752        | -               |
| 0.9529 | 12150 | 0.0129        | -               |
| 0.9569 | 12200 | 0.1644        | -               |
| 0.9608 | 12250 | 0.0305        | -               |
| 0.9647 | 12300 | 0.0133        | -               |
| 0.9686 | 12350 | 0.0687        | -               |
| 0.9725 | 12400 | 0.0039        | -               |
| 0.9765 | 12450 | 0.1179        | -               |
| 0.9804 | 12500 | 0.1867        | -               |
| 0.9843 | 12550 | 0.0225        | -               |
| 0.9882 | 12600 | 0.1914        | -               |
| 0.9922 | 12650 | 0.0592        | -               |
| 0.9961 | 12700 | 0.0059        | -               |
| 1.0    | 12750 | 0.1016        | 0.2295          |

### 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}
}
```

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