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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- Ramyashree/Dataset-train500-test100withwronginput
metrics:
- accuracy
widget:
- text: I weant to use my other account, switch them
- text: I can't remember my password, help me reset it
- text: the game was postponed and i wanna get a reimbursement
- text: where to change to another online account
- text: the show was cancelled, get a reimbursement
pipeline_tag: text-classification
inference: true
base_model: thenlper/gte-large
model-index:
- name: SetFit with thenlper/gte-large
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Ramyashree/Dataset-train500-test100withwronginput
type: Ramyashree/Dataset-train500-test100withwronginput
split: test
metrics:
- type: accuracy
value: 0.94
name: Accuracy
---
# SetFit with thenlper/gte-large
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [Ramyashree/Dataset-train500-test100withwronginput](https://huggingface.co/datasets/Ramyashree/Dataset-train500-test100withwronginput) dataset that can be used for Text Classification. This SetFit model uses [thenlper/gte-large](https://huggingface.co/thenlper/gte-large) 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:** [thenlper/gte-large](https://huggingface.co/thenlper/gte-large)
- **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:** 10 classes
- **Training Dataset:** [Ramyashree/Dataset-train500-test100withwronginput](https://huggingface.co/datasets/Ramyashree/Dataset-train500-test100withwronginput)
<!-- - **Language:** 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 |
|:--------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| create_account | <ul><li>"I don't have an online account, what do I have to do to register?"</li><li>'can you tell me if i can regisger two accounts with a single email address?'</li><li>'I have no online account, open one, please'</li></ul> |
| edit_account | <ul><li>'how can I modify the information on my profile?'</li><li>'can u ask an agent how to make changes to my profile?'</li><li>'I want to update the information on my profile'</li></ul> |
| delete_account | <ul><li>'can I close my account?'</li><li>"I don't want my account, can you delete it?"</li><li>'how do i close my online account?'</li></ul> |
| switch_account | <ul><li>'I would like to use my other online account , could you switch them, please?'</li><li>'i want to use my other online account, can u change them?'</li><li>'how do i change to another account?'</li></ul> |
| get_invoice | <ul><li>'what can you tell me about getting some bills?'</li><li>'tell me where I can request a bill'</li><li>'ask an agent if i can obtain some bills'</li></ul> |
| get_refund | <ul><li>'the game was postponed, help me obtain a reimbursement'</li><li>'the game was postponed, what should I do to obtain a reimbursement?'</li><li>'the concert was postponed, what should I do to request a reimbursement?'</li></ul> |
| payment_issue | <ul><li>'i have an issue making a payment with card and i want to inform of it, please'</li><li>'I got an error message when I attempted to pay, but my card was charged anyway and I want to notify it'</li><li>'I want to notify a problem making a payment, can you help me?'</li></ul> |
| check_refund_policy | <ul><li>"I'm interested in your reimbursement polivy"</li><li>'i wanna see your refund policy, can u help me?'</li><li>'where do I see your money back policy?'</li></ul> |
| recover_password | <ul><li>'my online account was hacked and I want tyo get it back'</li><li>"I lost my password and I'd like to retrieve it, please"</li><li>'could u ask an agent how i can reset my password?'</li></ul> |
| track_refund | <ul><li>'tell me if my refund was processed'</li><li>'I need help checking the status of my refund'</li><li>'I want to see the status of my refund, can you help me?'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.94 |
## 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("Ramyashree/gte-large-train-test-3")
# Run inference
preds = model("where to change to another online account")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 10.258 | 24 |
| Label | Training Sample Count |
|:--------------------|:----------------------|
| check_refund_policy | 50 |
| create_account | 50 |
| delete_account | 50 |
| edit_account | 50 |
| get_invoice | 50 |
| get_refund | 50 |
| payment_issue | 50 |
| recover_password | 50 |
| switch_account | 50 |
| track_refund | 50 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0008 | 1 | 0.3248 | - |
| 0.04 | 50 | 0.1606 | - |
| 0.08 | 100 | 0.0058 | - |
| 0.12 | 150 | 0.0047 | - |
| 0.16 | 200 | 0.0009 | - |
| 0.2 | 250 | 0.0007 | - |
| 0.24 | 300 | 0.001 | - |
| 0.28 | 350 | 0.0008 | - |
| 0.32 | 400 | 0.0005 | - |
| 0.36 | 450 | 0.0004 | - |
| 0.4 | 500 | 0.0005 | - |
| 0.44 | 550 | 0.0005 | - |
| 0.48 | 600 | 0.0006 | - |
| 0.52 | 650 | 0.0005 | - |
| 0.56 | 700 | 0.0004 | - |
| 0.6 | 750 | 0.0004 | - |
| 0.64 | 800 | 0.0002 | - |
| 0.68 | 850 | 0.0003 | - |
| 0.72 | 900 | 0.0002 | - |
| 0.76 | 950 | 0.0002 | - |
| 0.8 | 1000 | 0.0003 | - |
| 0.84 | 1050 | 0.0002 | - |
| 0.88 | 1100 | 0.0002 | - |
| 0.92 | 1150 | 0.0003 | - |
| 0.96 | 1200 | 0.0003 | - |
| 1.0 | 1250 | 0.0003 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- 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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