metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
I recently purchased the Reevati Gold Pearl Necklace and upon receiving
it, I noticed that the pearls are not properly aligned and some seem to be
of different sizes. This is not what I expected based on the images on
your site.
- text: >-
I recently ordered the Once in a Blue Moon Statement Ring but haven't
received any shipping updates yet. Can you provide me with the current
status of my order?
- text: >-
I recently bought the Golden Love Affair Pendant, but it seems to have
tarnished very quickly. I'm not satisfied with the quality. What can you
do about this?
- text: >-
I recently purchased the Three Crystal Proposal Ring, but I'm disappointed
to find that one of the crystals is loose. Can you assist me with this
issue?
- text: >-
I recently purchased the Bloomingdale Pendant, but I've noticed that the
quality does not meet the standards promised on the website. The pendant
looks tarnished and is different from the images shown.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8024691358024691
name: Accuracy
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 Type: SetFit
- Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 4 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 |
---|---|
product faq |
|
product discoveribility |
|
order tracking |
|
product policy |
|
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
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("I recently purchased the Three Crystal Proposal Ring, but I'm disappointed to find that one of the crystals is loose. Can you assist me with this issue?")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 6 | 16.4474 | 30 |
Label | Training Sample Count |
---|---|
negative | 0 |
positive | 0 |
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 |
---|---|---|---|
0.0016 | 1 | 0.1464 | - |
0.0822 | 50 | 0.0907 | - |
0.1645 | 100 | 0.0059 | - |
0.2467 | 150 | 0.0013 | - |
0.3289 | 200 | 0.0009 | - |
0.4112 | 250 | 0.0007 | - |
0.4934 | 300 | 0.0004 | - |
0.5757 | 350 | 0.0003 | - |
0.6579 | 400 | 0.0001 | - |
0.7401 | 450 | 0.0002 | - |
0.8224 | 500 | 0.0002 | - |
0.9046 | 550 | 0.0002 | - |
0.9868 | 600 | 0.0001 | - |
1.0 | 608 | - | 0.2272 |
1.0691 | 650 | 0.0001 | - |
1.1513 | 700 | 0.0001 | - |
1.2336 | 750 | 0.0001 | - |
1.3158 | 800 | 0.0001 | - |
1.3980 | 850 | 0.0001 | - |
1.4803 | 900 | 0.0001 | - |
1.5625 | 950 | 0.0001 | - |
1.6447 | 1000 | 0.0001 | - |
1.7270 | 1050 | 0.0001 | - |
1.8092 | 1100 | 0.0 | - |
1.8914 | 1150 | 0.0001 | - |
1.9737 | 1200 | 0.0001 | - |
2.0 | 1216 | - | 0.2807 |
2.0559 | 1250 | 0.0001 | - |
2.1382 | 1300 | 0.0001 | - |
2.2204 | 1350 | 0.0001 | - |
2.3026 | 1400 | 0.0 | - |
2.3849 | 1450 | 0.0001 | - |
2.4671 | 1500 | 0.0001 | - |
2.5493 | 1550 | 0.0 | - |
2.6316 | 1600 | 0.0001 | - |
2.7138 | 1650 | 0.0 | - |
2.7961 | 1700 | 0.0001 | - |
2.8783 | 1750 | 0.0 | - |
2.9605 | 1800 | 0.0 | - |
3.0 | 1824 | - | 0.3011 |
3.0428 | 1850 | 0.0 | - |
3.125 | 1900 | 0.0001 | - |
3.2072 | 1950 | 0.0001 | - |
3.2895 | 2000 | 0.0 | - |
3.3717 | 2050 | 0.0001 | - |
3.4539 | 2100 | 0.0001 | - |
3.5362 | 2150 | 0.0 | - |
3.6184 | 2200 | 0.0001 | - |
3.7007 | 2250 | 0.0001 | - |
3.7829 | 2300 | 0.0 | - |
3.8651 | 2350 | 0.0 | - |
3.9474 | 2400 | 0.0001 | - |
4.0 | 2432 | - | 0.311 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.9.16
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.1
- Datasets: 2.15.0
- 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}
}