Tarssio/modelo_setfit_politica_BA

SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

• Model Type: SetFit
• Sentence Transformer body: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 128 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
Positive	'Enfim,Bonfim 🥳🥳🥳🥳🥳' '👏👏👏👏' 'Pequenas ações fazem sonhos realidades #OhBrabo 💙💙💙'
Negative	'@jeronimorodriguesba quando terá uma segunda convocação do concurso SECBA?' 'Cadê a MP do piso da enfermagem ministro' 'Sim !! A escola municipal aqui do bairro liberdade,30 crianças esperando até hoje as profissionais ADI para crianças que necessita acompanhamento..'

Evaluation

Metrics

Label	Accuracy
all	0.9043

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Tarssio/modelo_setfit_politica_BA")
# Run inference
preds = model("👏👏👏")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	19.4813	313

Label	Training Sample Count
Negative	175
Positive	199

Training Hyperparameters

• batch_size: (4, 4)
• num_epochs: (4, 4)
• 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: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0011	1	0.3616	-
0.0535	50	0.3129	-
0.1070	100	0.2912	-
0.1604	150	0.191	-
0.2139	200	0.0907	-
0.2674	250	0.0086	-
0.3209	300	0.0042	-
0.3743	350	0.0161	-
0.4278	400	0.0007	-
0.4813	450	0.0403	-
0.5348	500	0.0055	-
0.5882	550	0.0057	-
0.6417	600	0.0002	-
0.6952	650	0.0002	-
0.7487	700	0.0	-
0.8021	750	0.0026	-
0.8556	800	0.0002	-
0.9091	850	0.0002	-
0.9626	900	0.0004	-
1.0	935	-	0.1724
1.0160	950	0.0001	-
1.0695	1000	0.0006	-
1.1230	1050	0.0001	-
1.1765	1100	0.0008	-
1.2299	1150	0.0002	-
1.2834	1200	0.0001	-
1.3369	1250	0.0002	-
1.3904	1300	0.0002	-
1.4439	1350	0.0002	-
1.4973	1400	0.0002	-
1.5508	1450	0.0	-
1.6043	1500	0.0002	-
1.6578	1550	0.2178	-
1.7112	1600	0.0002	-
1.7647	1650	0.0001	-
1.8182	1700	0.0001	-
1.8717	1750	0.0003	-
1.9251	1800	0.0359	-
1.9786	1850	0.0001	-
2.0	1870	-	0.1601
2.0321	1900	0.0001	-
2.0856	1950	0.0002	-
2.1390	2000	0.0001	-
2.1925	2050	0.0001	-
2.2460	2100	0.0002	-
2.2995	2150	0.0002	-
2.3529	2200	0.0003	-
2.4064	2250	0.0001	-
2.4599	2300	0.0002	-
2.5134	2350	0.0001	-
2.5668	2400	0.0	-
2.6203	2450	0.0001	-
2.6738	2500	0.0	-
2.7273	2550	0.0001	-
2.7807	2600	0.0001	-
2.8342	2650	0.0	-
2.8877	2700	0.0	-
2.9412	2750	0.0	-
2.9947	2800	0.0001	-
3.0	2805	-	0.1568
3.0481	2850	0.0001	-
3.1016	2900	0.0001	-
3.1551	2950	0.0001	-
3.2086	3000	0.0001	-
3.2620	3050	0.0001	-
3.3155	3100	0.0045	-
3.3690	3150	0.0	-
3.4225	3200	0.0001	-
3.4759	3250	0.0002	-
3.5294	3300	0.0	-
3.5829	3350	0.0002	-
3.6364	3400	0.0	-
3.6898	3450	0.0	-
3.7433	3500	0.0002	-
3.7968	3550	0.0	-
3.8503	3600	0.0	-
3.9037	3650	0.0005	-
3.9572	3700	0.0001	-
4.0	3740	-	0.1574

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• SetFit: 1.0.3
• Sentence Transformers: 2.2.2
• Transformers: 4.35.2
• PyTorch: 2.1.0+cu121
• Datasets: 2.16.1
• Tokenizers: 0.15.0

Citation

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}
}