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classifier_woog_hkv

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
metrics:
- accuracy
widget:
- text: Can you show me sarees made of katan silk?
- text: Can I schedule the delivery for a specific date and time?
- text: Can I cancel my order and get a refund if it hasn't been shipped yet?
- text: How do the traditional hand-woven Banarasi sarees from HKV Benaras differ
from those made by machine-driven industries?
- text: cookie boxes with inserts
pipeline_tag: text-classification
inference: true
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: 0.9245283018867925
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:** 5 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 |
|:------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| general_faq | <ul><li>'What makes Banarasi silk sarees unique compared to other types of sarees, and what are their main varieties?'</li><li>'How to identify mashru silk'</li><li>'How can I verify the authenticity of Real Zari in a saree'</li></ul> |
| product discoverability | <ul><li>'bakery boxes with custom designs'</li><li>'What are the different fabric options available for sarees?'</li><li>'show me some trending sneakers under 25k'</li></ul> |
| product faq | <ul><li>'Is the Wmns Dunk Low Harvest Moon available in size 7?'</li><li>'What type of color is the Pure Katan silk Kadhwa Bootidaar Banarasi Saree?'</li><li>'What type of color is the Pure Katan Silk Pastel Orange Kadhwa Satin Tanchoi Banarasi Saree?'</li></ul> |
| product policy | <ul><li>'What is the policy for returning a product that was part of a special sale celebration?'</li><li>'Can I return an item if it was damaged during delivery preparation?'</li><li>'Do you offer express shipping for sneakers?'</li></ul> |
| order tracking | <ul><li>'I ordered the Cupcake Cases 3 days ago with order no 34567 how long will it take to deliver?'</li><li>'Do you provide shipping insurance for high-value orders?'</li><li>'My order has been shipped 1 day ago but still not out for delivery. Can you tell how long will it take to deliver?'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9245 |
## 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("Shankhdhar/classifier_woog_hkv")
# Run inference
preds = model("cookie boxes with inserts")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 4 | 11.9441 | 24 |
| Label | Training Sample Count |
|:------------------------|:----------------------|
| general_faq | 4 |
| order tracking | 28 |
| product discoverability | 40 |
| product faq | 40 |
| product policy | 31 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0010 | 1 | 0.3031 | - |
| 0.0517 | 50 | 0.1396 | - |
| 0.1033 | 100 | 0.0959 | - |
| 0.1550 | 150 | 0.0036 | - |
| 0.2066 | 200 | 0.0009 | - |
| 0.2583 | 250 | 0.0008 | - |
| 0.3099 | 300 | 0.0011 | - |
| 0.3616 | 350 | 0.0005 | - |
| 0.4132 | 400 | 0.0004 | - |
| 0.4649 | 450 | 0.0003 | - |
| 0.5165 | 500 | 0.0003 | - |
| 0.5682 | 550 | 0.0003 | - |
| 0.6198 | 600 | 0.0003 | - |
| 0.6715 | 650 | 0.0001 | - |
| 0.7231 | 700 | 0.0002 | - |
| 0.7748 | 750 | 0.0001 | - |
| 0.8264 | 800 | 0.0002 | - |
| 0.8781 | 850 | 0.0002 | - |
| 0.9298 | 900 | 0.0001 | - |
| 0.0010 | 1 | 0.0002 | - |
| 0.0517 | 50 | 0.0002 | - |
| 0.1033 | 100 | 0.0007 | - |
| 0.1550 | 150 | 0.0001 | - |
| 0.2066 | 200 | 0.0002 | - |
| 0.2583 | 250 | 0.0002 | - |
| 0.3099 | 300 | 0.0001 | - |
| 0.3616 | 350 | 0.0502 | - |
| 0.4132 | 400 | 0.0001 | - |
| 0.4649 | 450 | 0.0001 | - |
| 0.5165 | 500 | 0.0001 | - |
| 0.5682 | 550 | 0.0001 | - |
| 0.6198 | 600 | 0.0 | - |
| 0.6715 | 650 | 0.0 | - |
| 0.7231 | 700 | 0.0001 | - |
| 0.7748 | 750 | 0.0 | - |
| 0.8264 | 800 | 0.0001 | - |
| 0.8781 | 850 | 0.0001 | - |
| 0.9298 | 900 | 0.0001 | - |
| 0.9814 | 950 | 0.0001 | - |
| 1.0331 | 1000 | 0.0001 | - |
| 1.0847 | 1050 | 0.0001 | - |
| 1.1364 | 1100 | 0.0 | - |
| 1.1880 | 1150 | 0.0 | - |
| 1.2397 | 1200 | 0.0 | - |
| 1.2913 | 1250 | 0.0 | - |
| 1.3430 | 1300 | 0.0001 | - |
| 1.3946 | 1350 | 0.0 | - |
| 1.4463 | 1400 | 0.0 | - |
| 1.4979 | 1450 | 0.0 | - |
| 1.5496 | 1500 | 0.0 | - |
| 1.6012 | 1550 | 0.0 | - |
| 1.6529 | 1600 | 0.0 | - |
| 1.7045 | 1650 | 0.0 | - |
| 1.7562 | 1700 | 0.0001 | - |
| 1.8079 | 1750 | 0.0 | - |
| 1.8595 | 1800 | 0.0 | - |
| 1.9112 | 1850 | 0.0 | - |
| 1.9628 | 1900 | 0.0 | - |
| 0.0010 | 1 | 0.0 | - |
| 0.0517 | 50 | 0.0 | - |
| 0.1033 | 100 | 0.0001 | - |
| 0.1550 | 150 | 0.0 | - |
| 0.2066 | 200 | 0.0001 | - |
| 0.2583 | 250 | 0.0001 | - |
| 0.3099 | 300 | 0.0 | - |
| 0.3616 | 350 | 0.0402 | - |
| 0.4132 | 400 | 0.0001 | - |
| 0.4649 | 450 | 0.0 | - |
| 0.5165 | 500 | 0.0 | - |
| 0.5682 | 550 | 0.0 | - |
| 0.6198 | 600 | 0.0 | - |
| 0.6715 | 650 | 0.0 | - |
| 0.7231 | 700 | 0.0 | - |
| 0.7748 | 750 | 0.0 | - |
| 0.8264 | 800 | 0.0 | - |
| 0.8781 | 850 | 0.0 | - |
| 0.9298 | 900 | 0.0 | - |
| 0.9814 | 950 | 0.0 | - |
| 1.0331 | 1000 | 0.0 | - |
| 1.0847 | 1050 | 0.0 | - |
| 1.1364 | 1100 | 0.0 | - |
| 1.1880 | 1150 | 0.0 | - |
| 1.2397 | 1200 | 0.0 | - |
| 1.2913 | 1250 | 0.0 | - |
| 1.3430 | 1300 | 0.0 | - |
| 1.3946 | 1350 | 0.0 | - |
| 1.4463 | 1400 | 0.0 | - |
| 1.4979 | 1450 | 0.0 | - |
| 1.5496 | 1500 | 0.0 | - |
| 1.6012 | 1550 | 0.0 | - |
| 1.6529 | 1600 | 0.0 | - |
| 1.7045 | 1650 | 0.0 | - |
| 1.7562 | 1700 | 0.0 | - |
| 1.8079 | 1750 | 0.0 | - |
| 1.8595 | 1800 | 0.0 | - |
| 1.9112 | 1850 | 0.0 | - |
| 1.9628 | 1900 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.2.2+cu121
- Datasets: 2.20.0
- Tokenizers: 0.15.2
## 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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