metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: meedan/paraphrase-filipino-mpnet-base-v2
datasets:
  - bsen26/eyeR-classification-multi-label-category2
metrics:
  - accuracy
widget:
  - text: >-
      i ordered shake shake fries but they give me just the plain one!! there's
      no ketchup or any cutlery!!! i will only give you one star!! tsk poor
      service ??
  - text: >-
      The fries were soggy and did not taste good, there was no cutlery, the
      butter was already melted when I got the order.
  - text: >-
      i ordered crispy fillet ala king why no sauce ? and asked for iced tea and
      you give pineapple juice ? are you kidding me ? are you even reading some
      instructions?
  - text: Wrong coffee / no ketchup / cold fries. Ugh
  - text: >-
      They have forgot to put inside the toy i ordered, my child is dispointed
      because she's expecting the pikachu toy please fix this !!
pipeline_tag: text-classification
inference: false
model-index:
  - name: SetFit with meedan/paraphrase-filipino-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: bsen26/eyeR-classification-multi-label-category2
          type: bsen26/eyeR-classification-multi-label-category2
          split: test
        metrics:
          - type: accuracy
            value: 0.5407407407407407
            name: Accuracy

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

This is a SetFit model trained on the bsen26/eyeR-classification-multi-label-category2 dataset that can be used for Text Classification. This SetFit model uses meedan/paraphrase-filipino-mpnet-base-v2 as the Sentence Transformer embedding model. A OneVsRestClassifier 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 OneVsRestClassifier instance
Maximum Sequence Length: 128 tokens
Training Dataset: bsen26/eyeR-classification-multi-label-category2

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
all	0.5407

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-category2-multilabel")
# Run inference
preds = model("Wrong coffee / no ketchup / cold fries. Ugh")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	18.3958	41

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
0.0010	1	0.0919	-
0.0521	50	0.1443	-
0.1042	100	0.0682	-
0.1562	150	0.1043	-
0.2083	200	0.0653	-
0.2604	250	0.0136	-
0.3125	300	0.0025	-
0.3646	350	0.0195	-
0.4167	400	0.0073	-
0.4688	450	0.0115	-
0.5208	500	0.0045	-
0.5729	550	0.0052	-
0.625	600	0.0091	-
0.6771	650	0.0037	-
0.7292	700	0.0027	-
0.7812	750	0.0058	-
0.8333	800	0.0118	-
0.8854	850	0.0025	-
0.9375	900	0.0005	-
0.9896	950	0.0085	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.7.0
Transformers: 4.40.2
PyTorch: 2.2.1+cu121
Datasets: 2.19.1
Tokenizers: 0.19.1

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}
}