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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Please email the information to me.
- text: Give me a second, please.
- text: Is it possible to talk to a higher authority?
- text: Sorry, too busy to chat right now.
- text: I already own one, thanks.
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: 0.9333333333333333
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:** 25 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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 |
|:---------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| do_not_qualify | <ul><li>"Your target age group doesn't include me."</li><li>"I'm outside the age range for this."</li><li>"I'm not in the age group you're looking for."</li></ul> |
| can_you_email | <ul><li>'I prefer email, can you write to me?'</li><li>'Email is more convenient for me, can you use that?'</li><li>'Can you send me the details by email?'</li></ul> |
| say_again | <ul><li>'Can you repeat that, please?'</li><li>'I missed that, can you say it again?'</li><li>'Could you please repeat what you just said?'</li></ul> |
| hold_a_sec | <ul><li>'One moment, please hold.'</li><li>'Hang on for a bit, please.'</li><li>'Just a minute, please.'</li></ul> |
| language_barrier | <ul><li>'English is hard for me, ¿puedo hablar en español?'</li><li>'I struggle with English, ¿puede ser en español?'</li><li>"I'm more comfortable in Spanish, ¿podemos continuar en español?"</li></ul> |
| decline | <ul><li>'wrong'</li><li>'Never'</li><li>"I don't want this, thank you."</li></ul> |
| transfer_request | <ul><li>'Can you transfer this call to your superior?'</li><li>'I need to speak with someone in charge.'</li><li>'Can I speak with your manager?'</li></ul> |
| scam | <ul><li>"I'm skeptical, this doesn't sound right."</li><li>"I'm wary, this feels like a scam."</li><li>"Are you sure this isn't a scam?"</li></ul> |
| who_are_you | <ul><li>"I would like to know who's calling."</li><li>"Who's calling, please?"</li><li>'Who are you and why are you calling?'</li></ul> |
| where_did_you_get_my_info | <ul><li>'Can you explain how you got my contact info?'</li><li>"What's the source of my details you have?"</li><li>"I didn't give you my number, where did you get it?"</li></ul> |
| do_not_call | <ul><li>"Stop calling me, it's annoying!"</li><li>"I don't want to be contacted again."</li><li>"Enough calls, I'm not interested!"</li></ul> |
| where_are_you_calling_from | <ul><li>'Where are you calling from?'</li><li>'From which city or country are you calling?'</li><li>'Could you inform me of your current location?'</li></ul> |
| complain_calls | <ul><li>"Too many calls like this, it's irritating."</li><li>"I've had several calls like this, it's annoying."</li><li>"I keep getting these calls, it's too much."</li></ul> |
| busy | <ul><li>"Right now isn't good, I'm busy with something."</li><li>"I'm swamped at the moment, sorry."</li><li>"I'm busy right now, can't talk."</li></ul> |
| greetings | <ul><li>'Hi, how can I help you?'</li><li>'Hello, what can I help you with today?'</li><li>'Hello, yes?'</li></ul> |
| sorry_greeting | <ul><li>"I'm not at my best, what do you need?"</li><li>"Sorry, it's a bad time, I'm sick."</li><li>"Not a great time, I'm dealing with a personal issue."</li></ul> |
| GreetBack | <ul><li>'Doing well, how about yourself?'</li><li>'Pretty good, what about you?'</li><li>"Not bad, and how's it going on your end?"</li></ul> |
| calling_about | <ul><li>'Why are you calling me?'</li><li>"What's the matter, why the call?"</li><li>'May I know the reason for your call?'</li></ul> |
| answering_machine | <ul><li>"Leave a message and I'll get back to you."</li><li>"You're speaking to an answering machine, leave a message."</li><li>"This is an answering machine, I'm not available."</li></ul> |
| weather | <ul><li>"Sunny skies here, what's it like where you are?"</li><li>"It's a bit cloudy here, is it the same there?"</li><li>"It's warm here, what about where you are?"</li></ul> |
| are_you_bot | <ul><li>'Is this a bot calling me?'</li><li>'Is this a recorded message or are you real?'</li><li>'Are you a live person or a recording?'</li></ul> |
| affirmation | <ul><li>'yes'</li><li>"That's true, yes."</li><li>"Precisely, that's right."</li></ul> |
| not_interested | <ul><li>"This doesn't interest me, sorry."</li><li>"This offer isn't relevant to my interests."</li><li>"Thanks, but this isn't something I need."</li></ul> |
| already | <ul><li>"I've made this purchase before."</li><li>"This isn't new to me, I have it already."</li><li>"I've been using this for a while now."</li></ul> |
| abusibve | <ul><li>"This is unacceptable, I won't tolerate this!"</li><li>'I demand you stop this abusive calling!'</li><li>"Stop calling me, it's harassment!"</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9333 |
## 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("Give me a second, please.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 6.8375 | 13 |
| Label | Training Sample Count |
|:---------------------------|:----------------------|
| GreetBack | 9 |
| abusibve | 9 |
| affirmation | 10 |
| already | 10 |
| answering_machine | 8 |
| are_you_bot | 8 |
| busy | 9 |
| calling_about | 8 |
| can_you_email | 11 |
| complain_calls | 11 |
| decline | 10 |
| do_not_call | 12 |
| do_not_qualify | 9 |
| greetings | 8 |
| hold_a_sec | 8 |
| language_barrier | 10 |
| not_interested | 11 |
| say_again | 12 |
| scam | 9 |
| sorry_greeting | 9 |
| transfer_request | 8 |
| weather | 10 |
| where_are_you_calling_from | 9 |
| where_did_you_get_my_info | 11 |
| who_are_you | 11 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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 | 0.1054 | - |
| 0.0417 | 50 | 0.1111 | - |
| 0.0833 | 100 | 0.0798 | - |
| 0.125 | 150 | 0.0826 | - |
| 0.1667 | 200 | 0.0308 | - |
| 0.2083 | 250 | 0.0324 | - |
| 0.25 | 300 | 0.0607 | - |
| 0.2917 | 350 | 0.0042 | - |
| 0.3333 | 400 | 0.0116 | - |
| 0.375 | 450 | 0.0049 | - |
| 0.4167 | 500 | 0.0154 | - |
| 0.4583 | 550 | 0.0158 | - |
| 0.5 | 600 | 0.0036 | - |
| 0.5417 | 650 | 0.001 | - |
| 0.5833 | 700 | 0.0015 | - |
| 0.625 | 750 | 0.0012 | - |
| 0.6667 | 800 | 0.0009 | - |
| 0.7083 | 850 | 0.0008 | - |
| 0.75 | 900 | 0.0008 | - |
| 0.7917 | 950 | 0.0014 | - |
| 0.8333 | 1000 | 0.0005 | - |
| 0.875 | 1050 | 0.0027 | - |
| 0.9167 | 1100 | 0.0007 | - |
| 0.9583 | 1150 | 0.0008 | - |
| 1.0 | 1200 | 0.0012 | - |
| 1.0417 | 1250 | 0.0012 | - |
| 1.0833 | 1300 | 0.0006 | - |
| 1.125 | 1350 | 0.0005 | - |
| 1.1667 | 1400 | 0.0003 | - |
| 1.2083 | 1450 | 0.0012 | - |
| 1.25 | 1500 | 0.0006 | - |
| 1.2917 | 1550 | 0.0008 | - |
| 1.3333 | 1600 | 0.0008 | - |
| 1.375 | 1650 | 0.0003 | - |
| 1.4167 | 1700 | 0.0004 | - |
| 1.4583 | 1750 | 0.0005 | - |
| 1.5 | 1800 | 0.0004 | - |
| 1.5417 | 1850 | 0.0004 | - |
| 1.5833 | 1900 | 0.0008 | - |
| 1.625 | 1950 | 0.0004 | - |
| 1.6667 | 2000 | 0.0004 | - |
| 1.7083 | 2050 | 0.0021 | - |
| 1.75 | 2100 | 0.0004 | - |
| 1.7917 | 2150 | 0.0002 | - |
| 1.8333 | 2200 | 0.0006 | - |
| 1.875 | 2250 | 0.0004 | - |
| 1.9167 | 2300 | 0.0006 | - |
| 1.9583 | 2350 | 0.0006 | - |
| 2.0 | 2400 | 0.0003 | - |
| 2.0417 | 2450 | 0.0002 | - |
| 2.0833 | 2500 | 0.0002 | - |
| 2.125 | 2550 | 0.0003 | - |
| 2.1667 | 2600 | 0.0004 | - |
| 2.2083 | 2650 | 0.0004 | - |
| 2.25 | 2700 | 0.0005 | - |
| 2.2917 | 2750 | 0.0005 | - |
| 2.3333 | 2800 | 0.0005 | - |
| 2.375 | 2850 | 0.0007 | - |
| 2.4167 | 2900 | 0.0002 | - |
| 2.4583 | 2950 | 0.0003 | - |
| 2.5 | 3000 | 0.0004 | - |
| 2.5417 | 3050 | 0.0002 | - |
| 2.5833 | 3100 | 0.0004 | - |
| 2.625 | 3150 | 0.0002 | - |
| 2.6667 | 3200 | 0.0002 | - |
| 2.7083 | 3250 | 0.0003 | - |
| 2.75 | 3300 | 0.0002 | - |
| 2.7917 | 3350 | 0.0002 | - |
| 2.8333 | 3400 | 0.0003 | - |
| 2.875 | 3450 | 0.0002 | - |
| 2.9167 | 3500 | 0.0002 | - |
| 2.9583 | 3550 | 0.0002 | - |
| 3.0 | 3600 | 0.0002 | - |
### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.0
- PyTorch: 2.1.0
- Datasets: 2.14.6
- Tokenizers: 0.14.1
## 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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