bsen26/eyeR-category2-multilabel

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:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. 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}
}