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lucienbaumgartner/
PAG-annotation

Name: PAG-annotation
Rating: 2.5 (9 reviews)

setfit

safetensors

mpnet

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/all-mpnet-base-v2

finetune

model-index

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

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A MultiOutputClassifier 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/all-mpnet-base-v2
Classification head: a MultiOutputClassifier instance
Maximum Sequence Length: 384 tokens
Number of Classes: 3 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Evaluation

Metrics

Label	Accuracy	Precision	Recall	F1
all	0.5	0.8	0.8889	0.8421

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("setfit_model_id")
# Run inference
preds = model("Well done on orchestrating such a seamless event!")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	6	10.75	16

Training Hyperparameters

batch_size: (32, 2)
num_epochs: (10, 10)
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
0.0769	1	0.3115	-
1.0	13	-	0.1928
2.0	26	-	0.1831
3.0	39	-	0.1724
3.8462	50	0.08	-
4.0	52	-	0.1614
5.0	65	-	0.1695
6.0	78	-	0.1837
7.0	91	-	0.1904
7.6923	100	0.0364	-
8.0	104	-	0.1997
9.0	117	-	0.1994
10.0	130	-	0.1967

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.1
SetFit: 1.0.3
Sentence Transformers: 2.7.0
Transformers: 4.37.2
PyTorch: 2.2.0
Datasets: 2.19.1
Tokenizers: 0.15.1

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}
}

lucienbaumgartner/PAG-annotation