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jewellery_query_setfit_classifier

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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jewellery_query_setfit_classifier / README.md
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---
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](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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](https://www.sbert.net) 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](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 4 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| product faq | <ul><li>'What are the different sizes available for the Love is in the Air Proposal Ring, and do they come at different price points?'</li><li>'What is the material of the Open Pear Cut Ring and are there different sizes available?'</li><li>'What is the material used for making the Golden Spin Hoop Earring, and does it come with any kind of warranty or guarantee?'</li></ul> |
| product discoveribility | <ul><li>'What are the latest choker styles available for a wedding occasion?'</li><li>"I'm interested in sustainable jewelry; do you have any eco-friendly necklaces?"</li><li>'Could you recommend some necklaces with a vintage vibe to them?'</li></ul> |
| order tracking | <ul><li>'I recently purchased the Seher Pearl Choker Set and I would like to know the current status of my order delivery.'</li><li>"I placed an order for the Tiara Silver Ring, but I haven't received any shipping updates yet. Can you provide me with the current status of my order?"</li><li>'I recently ordered the Toes Of Love Pendant but have not received any shipping confirmation. Could you please provide me with the tracking details?'</li></ul> |
| product policy | <ul><li>'Are there any restocking fees for bracelet returns?'</li><li>"Can I exchange a ring if it doesn't fit properly?"</li><li>'Are there any care instructions included with the purchase of a ring?'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8025 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
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?")
```
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## 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
```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}
}
```
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