SetFit with pysentimiento/robertuito-sentiment-analysis

This is a SetFit model that can be used for Text Classification. This SetFit model uses pysentimiento/robertuito-sentiment-analysis 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: pysentimiento/robertuito-sentiment-analysis
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
0	"Aquest text és ofensiu o violent o negatiu o inapropiat per a un cercador de tràmits d'un ajuntament" "Aquest text és ofensiu o violent o negatiu o inapropiat per a un cercador de tràmits d'un ajuntament" "Aquest text és ofensiu o violent o negatiu o inapropiat per a un cercador de tràmits d'un ajuntament"
1	"Aquest text és valid per a un cercador de tràmits d'un ajuntament" "Aquest text és valid per a un cercador de tràmits d'un ajuntament" "Aquest text és valid per a un cercador de tràmits d'un ajuntament"

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("adriansanz/sentimentv2")
# Run inference
preds = model("Aquest text és valid per a un cercador de tràmits d'un ajuntament")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	12	15.0	18

Label	Training Sample Count
0	20
1	20

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (4, 4)
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: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0189	1	0.2722	-
0.9434	50	0.0004	-
1.8868	100	0.0003	-
2.8302	150	0.0002	-
3.7736	200	0.0001	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 3.0.1
Transformers: 4.39.0
PyTorch: 2.4.0+cu121
Datasets: 2.21.0
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}
}

adriansanz
/

sentimentv2