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rovargasc/
modelopruebaIA

Name: modelopruebaIA
Availability: InStock

setfit

safetensors

mpnet

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/paraphrase-mpnet-base-v2

finetune

model-index

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SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2
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
0	'joyless ' "to the movie 's contrived , lame screenplay and listless direction " "demonstrate how desperate the makers of this ` we 're - doing-it-for - the-cash ' sequel were "
1	'smart and newfangled ' 'weighty revelations , flowery dialogue , and nostalgia for the past ' 'wise and powerful '

Evaluation

Metrics

Label	Accuracy
all	0.8589

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("rovargasc/modelopruebaIA")
# Run inference
preds = model("chokes on its own depiction of upper-crust decorum . ")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	9.6	29

Label	Training Sample Count
0	20
1	20

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (2, 2)
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
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.01	1	0.332	-
0.5	50	0.143	-
1.0	100	0.0018	-
1.5	150	0.0009	-
2.0	200	0.0009	-

Framework Versions

Python: 3.11.13
SetFit: 1.1.2
Sentence Transformers: 4.1.0
Transformers: 4.53.0
PyTorch: 2.6.0+cu124
Datasets: 3.6.0
Tokenizers: 0.21.2

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}
}