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

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:

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: 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.

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

@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}
}