SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-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:
- 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 Sources
Model Labels
Label |
Examples |
product policy |
- 'If I receive a defective Choker, what is the process to get a replacement?'
- 'Are there any restocking fees for returning a Choker?'
- 'What warranty do you offer on Choker products?'
|
product faq |
- 'What sizes is the Sheer Heart Ring available in, and can you provide the price for each size?'
- 'Is the Silver Eye Pendant nickel-free and hypoallergenic?'
- 'What material is used for the Crystal Drop Earring, and how should I take care of it to prevent tarnishing?'
|
order tracking |
- "I haven't received an update on my order status for the Rosé Bloom Ring. Could you please provide me with the tracking details?"
- "I recently ordered the Pakhi Handcrafted Earring but I haven't received any shipping confirmation. Could you please update me on the status of my order?"
- "I recently ordered a Whispering Star Silver Ring, but I haven't received any shipment updates. Can you please provide me with the status of my order?"
|
product discoveribility |
- 'What are the latest trends in bracelets that you have in stock?'
- "I'm interested in pendant sets from your 'Gold Plated Jewellery' collection. What options do you offer?"
- "I'm interested in silver bracelets. What options are available in that material?"
|
Evaluation
Metrics
Label |
Accuracy |
all |
0.8025 |
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
model = SetFitModel.from_pretrained("setfit_model_id")
preds = model("What are the latest trends in bracelets that you have in stock?")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
8 |
16.8438 |
31 |
Label |
Training Sample Count |
order tracking |
8 |
product discoveribility |
8 |
product faq |
8 |
product policy |
8 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
Epoch |
Step |
Training Loss |
Validation Loss |
0.0208 |
1 |
0.1273 |
- |
1.0417 |
50 |
0.004 |
- |
2.0833 |
100 |
0.0005 |
- |
3.125 |
150 |
0.0005 |
- |
Framework Versions
- Python: 3.9.16
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.1
- PyTorch: 2.3.0
- Datasets: 2.19.0
- 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}
}