wikd/test_model

SetFit with distilbert/distilbert-base-uncased-finetuned-sst-2-english

This is a SetFit model trained on the wikd/customer_data dataset that can be used for Text Classification. This SetFit model uses distilbert/distilbert-base-uncased-finetuned-sst-2-english 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: distilbert/distilbert-base-uncased-finetuned-sst-2-english
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 2 classes
• Training Dataset: wikd/customer_data

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 need to speak to a real person, not a dumb machine.' 'Stop with the automated nonsense and connect me to a human!' 'Your automated system is beyond frustrating, let me talk to someone!'
1	'I love your new product!' 'The delivery was very quick!' 'I would recommend this company to a friend'

Evaluation

Metrics

Label	Accuracy
all	1.0

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("The product is out of stock")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	10.0	14

Label	Training Sample Count
0	46
1	6

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
0.0077	1	0.1479	-
0.3846	50	0.0008	-
0.7692	100	0.0005	-

Framework Versions

• Python: 3.11.8
• SetFit: 1.0.3
• Sentence Transformers: 2.5.1
• Transformers: 4.38.2
• PyTorch: 2.2.1
• 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}
}