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saraestevez/
setfit-xlm-bank-tweets-processed-80

Name: setfit-xlm-bank-tweets-processed-80
Availability: InStock

setfit

safetensors

xlm-roberta

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

FacebookAI/xlm-roberta-base

finetune

model-index

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SetFit with FacebookAI/xlm-roberta-base

This is a SetFit model that can be used for Text Classification. This SetFit model uses FacebookAI/xlm-roberta-base 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: FacebookAI/xlm-roberta-base
Classification head: a LogisticRegression instance
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

discard

'Las negociaciones para el Banco de España avanzan rápidamente y el Congreso convocará el jueves la comisión de economía para anunciar los nombres pactados, con Conthe como la candidata más firme a gobernadora.'
'Depósitos y seguros son aspectos fundamentales para atraer clientes y potenciar el negocio de Caixabank en la segunda mitad del año.'
'El Banco Santander ofrece 400€ al cambiar tu nómina a su cuenta en línea, eliminando las comisiones bancarias.'

relevant

'Nuevo caso de phishing relacionado con Evobanco registrado el 13 de julio de 2024.'
'El Banco Sabadell ofrece depósitos a plazo fijo con un interés del 2,5% TAE a 1 año y 3% TAE a 6 meses, lo cual es una buena opción.'
'Estoy en Abanca porque no me cobran comisiones, de lo contrario ya los habría dejado.'

Evaluation

Metrics

Label	Accuracy
all	0.7138

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-xlm-bank-tweets-processed-80")
# Run inference
preds = model("Banco Sabadell confirma el día de pago de pensiones para jubilados en agosto.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	21.0437	36

Label	Training Sample Count
discard	80
relevant	80

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 20
body_learning_rate: (2e-05, 2e-05)
head_learning_rate: 2e-05
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
0.0025	1	0.4924	-
0.125	50	0.2519	-
0.25	100	0.186	-
0.375	150	0.188	-
0.5	200	0.0504	-
0.625	250	0.0412	-
0.75	300	0.0147	-
0.875	350	0.0517	-
1.0	400	0.0162	-

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}
}