mranonymaz/bol-topic-classifier

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

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-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:

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-multilingual-mpnet-base-v2
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 128 tokens
• Number of Classes: 2 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
customer_support	'Hoe neem ik contact op met bol klantenservice?' 'Kan ik chatten met de klantenservice van bol?' 'Wat is het telefoonnummer van bol klantenservice?'
do_not_forward	'Wat wordt het weer morgen in Amsterdam?' 'Gaat het vanmiddag regenen?' 'Hoe warm wordt het dit weekend?'

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("setfit_model_id")
# Run inference
preds = model("Mijn cadeaubon werkt niet.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	7.2457	45

Label	Training Sample Count
do_not_forward	421
customer_support	336

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (1, 1)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 5
• 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
• l2_weight: 0.01
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0021	1	0.3773	-
0.1055	50	0.2828	-
0.2110	100	0.16	-
0.3165	150	0.0341	-
0.4219	200	0.0118	-
0.5274	250	0.0045	-
0.6329	300	0.0014	-
0.7384	350	0.0005	-
0.8439	400	0.0007	-
0.9494	450	0.0003	-

Framework Versions

• Python: 3.11.4
• SetFit: 1.1.3
• Sentence Transformers: 5.0.0
• Transformers: 4.53.2
• PyTorch: 2.1.0
• Datasets: 2.15.0
• Tokenizers: 0.21.2

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}
}