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Funnyworld1412/
ABSA_review_game_genshin_impact-aspect

Name: ABSA_review_game_genshin_impact-aspect
Availability: InStock

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

firqaaa/setfit-indo-absa-restaurant-aspect

finetune

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SetFit Aspect Model with firqaaa/indo-setfit-absa-bert-base-restaurants-aspect

This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses firqaaa/indo-setfit-absa-bert-base-restaurants-aspect as the Sentence Transformer embedding model. A LogisticRegression 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:

Fine-tuning a Sentence Transformer with contrastive learning.
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:

Use a spaCy model to select possible aspect span candidates.
Use this SetFit model to filter these possible aspect span candidates.
Use a SetFit model to classify the filtered aspect span candidates.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: firqaaa/indo-setfit-absa-bert-base-restaurants-aspect
Classification head: a LogisticRegression instance
spaCy Model: id_core_news_trf
SetFitABSA Aspect Model: Funnyworld1412/ABSA_review_game_genshin_impact-aspect
SetFitABSA Polarity Model: Funnyworld1412/ABSA_review_game_genshin_impact-polarity
Maximum Sequence Length: 512 tokens
Number of Classes: 2 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label

Examples

aspect

'story:saranku developer menciptakan story menarik kehilangan player player yg bertahan repetitif monoton update size gede doang yg isinya chest itupun sampah puzzle yg rumit chest nya sampah story kebanyakan npc teyvat story utama mc dilupain gak difokusin map kalo udah kosong ya nyampah bikin size gede doang main 3 monoton perkembangan buruk'
'reward:tolong ditambah reward gachanya player kesulitan primo quest eksplorasi 100 dasar developer kapitalis game monoton ramah player kekurangan bahan gacha karakter'
'event:cuman saran pelit biar player gak kabur game sebelah hadiah event quest perbaiki udah nunggu event hadiah cuman gitu gitu aja sampek event selesai primogemnya 10 pull gacha gak tingakat kesulitan beda hadiah main kabur kalok pelit 1 jariang mohon perbaiki server indonya trimaksih'

no aspect

'saranku developer:saranku developer menciptakan story menarik kehilangan player player yg bertahan repetitif monoton update size gede doang yg isinya chest itupun sampah puzzle yg rumit chest nya sampah story kebanyakan npc teyvat story utama mc dilupain gak difokusin map kalo udah kosong ya nyampah bikin size gede doang main 3 monoton perkembangan buruk'
'story:saranku developer menciptakan story menarik kehilangan player player yg bertahan repetitif monoton update size gede doang yg isinya chest itupun sampah puzzle yg rumit chest nya sampah story kebanyakan npc teyvat story utama mc dilupain gak difokusin map kalo udah kosong ya nyampah bikin size gede doang main 3 monoton perkembangan buruk'
'kehilangan player player:saranku developer menciptakan story menarik kehilangan player player yg bertahan repetitif monoton update size gede doang yg isinya chest itupun sampah puzzle yg rumit chest nya sampah story kebanyakan npc teyvat story utama mc dilupain gak difokusin map kalo udah kosong ya nyampah bikin size gede doang main 3 monoton perkembangan buruk'

Uses

Direct Use for Inference

First install the SetFit library:

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_review_game_genshin_impact-aspect",
    "Funnyworld1412/ABSA_review_game_genshin_impact-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	31.2629	70

Label	Training Sample Count
no aspect	1049
aspect	324

Training Hyperparameters

batch_size: (4, 4)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 10
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	0.0089	-
0.0073	50	0.7206	-
0.0146	100	0.399	-
0.0218	150	0.0596	-
0.0291	200	0.3335	-
0.0364	250	0.1854	-
0.0437	300	0.0708	-
0.0510	350	0.0161	-
0.0583	400	0.3364	-
0.0655	450	0.0949	-
0.0728	500	0.1021	-
0.0801	550	0.3917	-
0.0874	600	0.0707	-
0.0947	650	0.3885	-
0.1020	700	0.046	-
0.1092	750	0.001	-
0.1165	800	0.0024	-
0.1238	850	0.2384	-
0.1311	900	0.0215	-
0.1384	950	0.2283	-
0.1457	1000	0.4564	-
0.1529	1050	0.0017	-
0.1602	1100	0.0612	-
0.1675	1150	0.2325	-
0.1748	1200	0.0568	-
0.1821	1250	0.0096	-
0.1894	1300	0.2803	-
0.1966	1350	0.0056	-
0.2039	1400	0.0107	-
0.2112	1450	0.0042	-
0.2185	1500	0.0636	-
0.2258	1550	0.0356	-
0.2331	1600	0.2264	-
0.2403	1650	0.2335	-
0.2476	1700	0.201	-
0.2549	1750	0.0386	-
0.2622	1800	0.0032	-
0.2695	1850	0.0023	-
0.2768	1900	0.0053	-
0.2840	1950	0.0228	-
0.2913	2000	0.0006	-
0.2986	2050	0.0003	-
0.3059	2100	0.0142	-
0.3132	2150	0.099	-
0.3205	2200	0.0144	-
0.3277	2250	0.0002	-
0.3350	2300	0.0042	-
0.3423	2350	0.0359	-
0.3496	2400	0.0004	-
0.3569	2450	0.0057	-
0.3642	2500	0.0046	-
0.3714	2550	0.0015	-
0.3787	2600	0.0023	-
0.3860	2650	0.0004	-
0.3933	2700	0.0002	-
0.4006	2750	0.0002	-
0.4079	2800	0.0267	-
0.4151	2850	0.0001	-
0.4224	2900	0.0003	-
0.4297	2950	0.0037	-
0.4370	3000	0.0005	-
0.4443	3050	0.0049	-
0.4516	3100	0.2431	-
0.4588	3150	0.2577	-
0.4661	3200	0.1556	-
0.4734	3250	0.1983	-
0.4807	3300	0.0884	-
0.4880	3350	0.0003	-
0.4953	3400	0.2302	-
0.5025	3450	0.0007	-
0.5098	3500	0.0002	-
0.5171	3550	0.0001	-
0.5244	3600	0.0845	-
0.5317	3650	0.0003	-
0.5390	3700	0.0001	-
0.5462	3750	0.0001	-
0.5535	3800	0.0	-
0.5608	3850	0.0001	-
0.5681	3900	0.001	-
0.5754	3950	0.0008	-
0.5827	4000	0.002	-
0.5899	4050	0.0002	-
0.5972	4100	0.1071	-
0.6045	4150	0.0001	-
0.6118	4200	0.0001	-
0.6191	4250	0.0001	-
0.6264	4300	0.0002	-
0.6336	4350	0.0001	-
0.6409	4400	0.0	-
0.6482	4450	0.2478	-
0.6555	4500	0.0	-
0.6628	4550	0.0003	-
0.6701	4600	0.0	-
0.6773	4650	0.0002	-
0.6846	4700	0.003	-
0.6919	4750	0.0007	-
0.6992	4800	0.0006	-
0.7065	4850	0.001	-
0.7138	4900	0.0106	-
0.7210	4950	0.0001	-
0.7283	5000	0.0002	-
0.7356	5050	0.0004	-
0.7429	5100	0.0008	-
0.7502	5150	0.0508	-
0.7575	5200	0.001	-
0.7647	5250	0.0	-
0.7720	5300	0.0249	-
0.7793	5350	0.0001	-
0.7866	5400	0.1026	-
0.7939	5450	0.0	-
0.8012	5500	0.0001	-
0.8084	5550	0.0028	-
0.8157	5600	0.0008	-
0.8230	5650	0.0002	-
0.8303	5700	0.0001	-
0.8376	5750	0.0	-
0.8449	5800	0.0001	-
0.8521	5850	0.0001	-
0.8594	5900	0.0094	-
0.8667	5950	0.0001	-
0.8740	6000	0.0	-
0.8813	6050	0.0	-
0.8886	6100	0.0	-
0.8958	6150	0.0001	-
0.9031	6200	0.0002	-
0.9104	6250	0.0026	-
0.9177	6300	0.1005	-
0.9250	6350	0.0002	-
0.9323	6400	0.0004	-
0.9395	6450	0.2456	-
0.9468	6500	0.0228	-
0.9541	6550	0.022	-
0.9614	6600	0.025	-
0.9687	6650	0.0002	-
0.9760	6700	0.0003	-
0.9832	6750	0.0001	-
0.9905	6800	0.0	-
0.9978	6850	0.1145	-
1.0	6865	-	0.1868

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}
}