SetFit with meedan/paraphrase-filipino-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses meedan/paraphrase-filipino-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: meedan/paraphrase-filipino-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 128 tokens
Number of Classes: 6 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	'I specifically asked for no onions, yet my sandwich was loaded with them when delivered.' 'The delivery driver spilled half my order all over the bag. What a mess!' 'Two hour wait only for my pizza to arrive burnt on the bottom from sitting too long.'
2	'Found a long strand of hair hanging out of my sealed takeout burger container.' 'Bits of plastic were baked into the crust of the takeout pizza I received.' 'The takeout container for my soup was leaking and left a trail of foul-smelling liquid.'
1	'Sobrang luto at tigas na para bang kahoy ang aking karne.' 'Sobrang lata ng pagkaluto, hindi na makain ang aking litsong manok.' 'Pizza crust was burnt black on the bottom yet still doughy raw on top.'
3	'Half the ingredients were missing from my order like they forgot to include them.' 'Binayaran ko ang dami, pero napakaliit lang ng portion size na naibigay sa akin.' 'The plate looked full but it was all rice, with small paltry portions of the main items.'
4	'Bland, overcooked chicken, soggy vegetables and hard, stale naan bread.' 'Tiny portion sizes, freezing cold plates, and a hair baked into the bread.' 'Every single thing I tried to order was met with confusion, attitude and mistakes.'
5	'From the appetizer to dessert, everything was prepared flawlessly. 10/10!' "The chilaquiles were authentic, flavor-packed and easily the best I've had." 'You can really taste the freshness of the local ingredients in every bite.'

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("bsen26/eyeR-classification-model-1.0")
# Run inference
preds = model("delivery and food preparation was suoer fast. nice")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	12.6833	17

Label	Training Sample Count
0	20
1	20
2	20
3	20
4	20
5	20

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
0.0033	1	0.2048	-
0.1667	50	0.048	-
0.3333	100	0.0148	-
0.5	150	0.0011	-
0.6667	200	0.0009	-
0.8333	250	0.0005	-
1.0	300	0.0008	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.6.1
Transformers: 4.38.2
PyTorch: 2.2.1+cu121
Datasets: 2.18.0
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}
}

bsen26
/

eyeR-classification-model-1.0