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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: '@SunderCR two hours of Sandstorm remixes. All merged together. No between-song
silence.'
- text: Discovered Plane Debris Is From Missing Malaysia Airlines Flight 370 | TIME
http://t.co/7fSn1GeWUX
- text: '#?? #???? #??? #??? MH370: Aircraft debris found on La Reunion is from missing
Malaysia Airlines ... http://t.co/oTsM38XMas'
- text: 'Today your life could change forever - #Chronicillness can''t be avoided
- It can be survived
Join #MyLifeStory >>> http://t.co/FYJWjDkM5I'
- text: SHOUOUT TO @kasad1lla CAUSE HER VOCALS ARE BLAZING HOT LIKE THE WEATHER SHES
IN
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/all-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8058161350844277
name: Accuracy
---
# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 2 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 |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | <ul><li>'To fight bioterrorism sir.'</li><li>'85V-265V 10W LED Warm White Light Motion Sensor Outdoor Flood Light PIR Lamp AUC http://t.co/NJVPXzMj5V http://t.co/Ijd7WzV5t9'</li><li>'Photo: referencereference: xekstrin: I THOUGHT THE NOSTRILS WERE EYES AND I ALMOST CRIED FROM FEAR partake... http://t.co/O7yYjLuKfJ'</li></ul> |
| 1 | <ul><li>'Police officer wounded suspect dead after exchanging shots: RICHMOND Va. (AP) \x89ÛÓ A Richmond police officer wa... http://t.co/Y0qQS2L7bS'</li><li>"There's a weird siren going off here...I hope Hunterston isn't in the process of blowing itself to smithereens..."</li><li>'Iranian warship points weapon at American helicopter... http://t.co/cgFZk8Ha1R'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8058 |
## 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("pEpOo/catastrophy6")
# Run inference
preds = model("SHOUOUT TO @kasad1lla CAUSE HER VOCALS ARE BLAZING HOT LIKE THE WEATHER SHES IN")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 14.7175 | 54 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 1335 |
| 1 | 948 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0094 | 1 | 0.0044 | - |
| 0.4717 | 50 | 0.005 | - |
| 0.9434 | 100 | 0.0007 | - |
| 0.0002 | 1 | 0.4675 | - |
| 0.0088 | 50 | 0.3358 | - |
| 0.0175 | 100 | 0.2516 | - |
| 0.0263 | 150 | 0.2158 | - |
| 0.0350 | 200 | 0.1924 | - |
| 0.0438 | 250 | 0.1907 | - |
| 0.0526 | 300 | 0.2166 | - |
| 0.0613 | 350 | 0.2243 | - |
| 0.0701 | 400 | 0.0644 | - |
| 0.0788 | 450 | 0.1924 | - |
| 0.0876 | 500 | 0.166 | - |
| 0.0964 | 550 | 0.2117 | - |
| 0.1051 | 600 | 0.0793 | - |
| 0.1139 | 650 | 0.0808 | - |
| 0.1226 | 700 | 0.1183 | - |
| 0.1314 | 750 | 0.0808 | - |
| 0.1402 | 800 | 0.0194 | - |
| 0.1489 | 850 | 0.0699 | - |
| 0.1577 | 900 | 0.0042 | - |
| 0.1664 | 950 | 0.0048 | - |
| 0.1752 | 1000 | 0.1886 | - |
| 0.1840 | 1050 | 0.0008 | - |
| 0.1927 | 1100 | 0.0033 | - |
| 0.2015 | 1150 | 0.0361 | - |
| 0.2102 | 1200 | 0.12 | - |
| 0.2190 | 1250 | 0.0035 | - |
| 0.2278 | 1300 | 0.0002 | - |
| 0.2365 | 1350 | 0.0479 | - |
| 0.2453 | 1400 | 0.0568 | - |
| 0.2540 | 1450 | 0.0004 | - |
| 0.2628 | 1500 | 0.0002 | - |
| 0.2715 | 1550 | 0.0013 | - |
| 0.2803 | 1600 | 0.0005 | - |
| 0.2891 | 1650 | 0.0014 | - |
| 0.2978 | 1700 | 0.0004 | - |
| 0.3066 | 1750 | 0.0008 | - |
| 0.3153 | 1800 | 0.0616 | - |
| 0.3241 | 1850 | 0.0003 | - |
| 0.3329 | 1900 | 0.001 | - |
| 0.3416 | 1950 | 0.0581 | - |
| 0.3504 | 2000 | 0.0657 | - |
| 0.3591 | 2050 | 0.0584 | - |
| 0.3679 | 2100 | 0.0339 | - |
| 0.3767 | 2150 | 0.0081 | - |
| 0.3854 | 2200 | 0.0001 | - |
| 0.3942 | 2250 | 0.0009 | - |
| 0.4029 | 2300 | 0.0018 | - |
| 0.4117 | 2350 | 0.0001 | - |
| 0.4205 | 2400 | 0.0012 | - |
| 0.4292 | 2450 | 0.0001 | - |
| 0.4380 | 2500 | 0.0003 | - |
| 0.4467 | 2550 | 0.0035 | - |
| 0.4555 | 2600 | 0.0172 | - |
| 0.4643 | 2650 | 0.0383 | - |
| 0.4730 | 2700 | 0.0222 | - |
| 0.4818 | 2750 | 0.0013 | - |
| 0.4905 | 2800 | 0.0007 | - |
| 0.4993 | 2850 | 0.0003 | - |
| 0.5081 | 2900 | 0.1247 | - |
| 0.5168 | 2950 | 0.023 | - |
| 0.5256 | 3000 | 0.0002 | - |
| 0.5343 | 3050 | 0.0002 | - |
| 0.5431 | 3100 | 0.0666 | - |
| 0.5519 | 3150 | 0.0002 | - |
| 0.5606 | 3200 | 0.0003 | - |
| 0.5694 | 3250 | 0.0012 | - |
| 0.5781 | 3300 | 0.0085 | - |
| 0.5869 | 3350 | 0.0003 | - |
| 0.5957 | 3400 | 0.0002 | - |
| 0.6044 | 3450 | 0.0004 | - |
| 0.6132 | 3500 | 0.013 | - |
| 0.6219 | 3550 | 0.0089 | - |
| 0.6307 | 3600 | 0.0001 | - |
| 0.6395 | 3650 | 0.0002 | - |
| 0.6482 | 3700 | 0.0039 | - |
| 0.6570 | 3750 | 0.0031 | - |
| 0.6657 | 3800 | 0.0009 | - |
| 0.6745 | 3850 | 0.0002 | - |
| 0.6833 | 3900 | 0.0002 | - |
| 0.6920 | 3950 | 0.0001 | - |
| 0.7008 | 4000 | 0.0 | - |
| 0.7095 | 4050 | 0.0212 | - |
| 0.7183 | 4100 | 0.0001 | - |
| 0.7270 | 4150 | 0.0586 | - |
| 0.7358 | 4200 | 0.0001 | - |
| 0.7446 | 4250 | 0.0003 | - |
| 0.7533 | 4300 | 0.0126 | - |
| 0.7621 | 4350 | 0.0001 | - |
| 0.7708 | 4400 | 0.0001 | - |
| 0.7796 | 4450 | 0.0001 | - |
| 0.7884 | 4500 | 0.0 | - |
| 0.7971 | 4550 | 0.0002 | - |
| 0.8059 | 4600 | 0.0002 | - |
| 0.8146 | 4650 | 0.0001 | - |
| 0.8234 | 4700 | 0.0035 | - |
| 0.8322 | 4750 | 0.0002 | - |
| 0.8409 | 4800 | 0.0002 | - |
| 0.8497 | 4850 | 0.0001 | - |
| 0.8584 | 4900 | 0.0001 | - |
| 0.8672 | 4950 | 0.0001 | - |
| 0.8760 | 5000 | 0.0003 | - |
| 0.8847 | 5050 | 0.0 | - |
| 0.8935 | 5100 | 0.0041 | - |
| 0.9022 | 5150 | 0.0001 | - |
| 0.9110 | 5200 | 0.0001 | - |
| 0.9198 | 5250 | 0.0001 | - |
| 0.9285 | 5300 | 0.0001 | - |
| 0.9373 | 5350 | 0.0001 | - |
| 0.9460 | 5400 | 0.0001 | - |
| 0.9548 | 5450 | 0.0001 | - |
| 0.9636 | 5500 | 0.0001 | - |
| 0.9723 | 5550 | 0.0001 | - |
| 0.9811 | 5600 | 0.0002 | - |
| 0.9898 | 5650 | 0.0271 | - |
| 0.9986 | 5700 | 0.0 | - |
### 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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