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saraestevez/
setfit-minilm-bank-tweets-processed-100

Name: setfit-minilm-bank-tweets-processed-100
Availability: InStock

setfit

safetensors

bert

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

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

finetune

model-index

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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:

Fine-tuning a Sentence Transformer with contrastive learning.
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

relevant

'Caixa y BBVA son los prestamistas que se benefician de la garantía en caso de impago en la financiación de inversiones, algo común también en las sociedades que avalan a comunidades autónomas.'
'El IBEX supera los 11.200 puntos gracias al impulso de la banca, liderado por Banco Sabadell con una subida del 2,05%.'
'Nuevo caso de phishing relacionado con ING, registrado el 16 de julio de 2024, con la URL /www.ingseguridad-app.com/es/login.'

discard

'El BBVA también tiene un mal servicio, ya que no aceptan billetes de 2.000 ni de 1.000 de San Martín, obligando a hacer largas filas tanto para cambiar como para depositar.'
'Merhaba, yaşadığınız deneyim için üzgünüz; Garanti BBVA ATM konum bilgilerini paylaşırsanız gerekli kontrolleri hızlıca yapacağız.'
'En la gasolinera sobre Constituyentes, mi tarjeta de crédito fue denegada y no me hicieron cargo en la aplicación.'

Evaluation

Metrics

Label	Accuracy
all	0.7594

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 SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("saraestevez/setfit-minilm-bank-tweets-processed-100")
# Run inference
preds = model("Los resultados del Banco Sabadell impulsan al IBEX 35.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	22.0	41

Label	Training Sample Count
discard	100
relevant	100

Training Hyperparameters

batch_size: (16, 2)
num_epochs: (1, 16)
max_steps: -1
sampling_strategy: oversampling
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
0.0008	1	0.3931	-
0.0396	50	0.2501	-
0.0792	100	0.2471	-
0.1188	150	0.1991	-
0.1584	200	0.0902	-
0.1979	250	0.0218	-
0.2375	300	0.0055	-
0.2771	350	0.0026	-
0.3167	400	0.0013	-
0.3563	450	0.0005	-
0.3959	500	0.0005	-
0.4355	550	0.001	-
0.4751	600	0.0003	-
0.5146	650	0.0003	-
0.5542	700	0.0001	-
0.5938	750	0.0003	-
0.6334	800	0.0003	-
0.6730	850	0.0004	-
0.7126	900	0.0002	-
0.7522	950	0.0001	-
0.7918	1000	0.0001	-
0.8314	1050	0.0001	-
0.8709	1100	0.0002	-
0.9105	1150	0.0002	-
0.9501	1200	0.0002	-
0.9897	1250	0.0	-

Framework Versions

Python: 3.11.0rc1
SetFit: 1.0.3
Sentence Transformers: 2.7.0
Transformers: 4.39.0
PyTorch: 2.3.1+cu121
Datasets: 2.19.1
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}
}