konsman/setfit-messages-multilabel-example

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

This is a SetFit model trained on the konsman/setfit-messages-optimized dataset that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-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:

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: sentence-transformers/paraphrase-mpnet-base-v2
• Classification head: a MultiOutputClassifier instance
• Maximum Sequence Length: 512 tokens
• Training Dataset: konsman/setfit-messages-optimized

Model Sources

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

Evaluation

Metrics

Label	F1	Accuracy
all	0.6897	0.3404

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("konsman/setfit-messages-multilabel-example")
# Run inference
preds = model("Sorry forgot to say that unfortunately after this problem that made me let sports and with the anxiety meds . I am now 83 kg")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	5	110.2344	469

Training Hyperparameters

• batch_size: (8, 8)
• num_epochs: (2, 2)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 5
• 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
0.0031	1	0.3209	-
0.1562	50	0.1823	-
0.3125	100	0.1003	-
0.4688	150	0.1774	-
0.625	200	0.0832	-
0.7812	250	0.0828	-
0.9375	300	0.0721	-
1.0938	350	0.1331	-
1.25	400	0.1215	-
1.4062	450	0.1494	-
1.5625	500	0.0444	-
1.7188	550	0.0688	-
1.875	600	0.1033	-
0.0125	1	0.0508	-
0.625	50	0.0793	-
1.25	100	0.081	-
1.875	150	0.1367	-

Framework Versions

• Python: 3.10.12
• SetFit: 1.0.2
• Sentence Transformers: 2.2.2
• Transformers: 4.35.2
• PyTorch: 2.1.0+cu121
• Datasets: 2.16.1
• Tokenizers: 0.15.0

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}
}