---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: "Rly tragedy in MP: Some live to recount horror: \x89ÛÏWhen I saw coaches\
\ of my train plunging into water I called my daughters and said t..."
- text: You must be annihilated!
- text: 'Severe Thunderstorms and Flash Flooding Possible in the Mid-South and Midwest
http://t.co/uAhIcWpIh4 #WEATHER #ENVIRONMENT #CLIMATE #NATURE'
- text: 'everyone''s wonder who will win and I''m over here wondering are those grapes
real ?????? #BB17'
- text: i swea it feels like im about to explode ??
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: 0.9203152364273205
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
### 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 | - 'To fight bioterrorism sir.'
- '85V-265V 10W LED Warm White Light Motion Sensor Outdoor Flood Light PIR Lamp AUC http://t.co/NJVPXzMj5V http://t.co/Ijd7WzV5t9'
- 'Photo: referencereference: xekstrin: I THOUGHT THE NOSTRILS WERE EYES AND I ALMOST CRIED FROM FEAR partake... http://t.co/O7yYjLuKfJ'
|
| 1 | - 'Police officer wounded suspect dead after exchanging shots: RICHMOND Va. (AP) \x89ÛÓ A Richmond police officer wa... http://t.co/Y0qQS2L7bS'
- "There's a weird siren going off here...I hope Hunterston isn't in the process of blowing itself to smithereens..."
- 'Iranian warship points weapon at American helicopter... http://t.co/cgFZk8Ha1R'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9203 |
## 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/catastrophy8")
# Run inference
preds = model("You must be annihilated!")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 14.5506 | 54 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 438 |
| 1 | 323 |
### Training Hyperparameters
- batch_size: (20, 20)
- 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0001 | 1 | 0.3847 | - |
| 0.0044 | 50 | 0.3738 | - |
| 0.0088 | 100 | 0.2274 | - |
| 0.0131 | 150 | 0.2747 | - |
| 0.0175 | 200 | 0.2251 | - |
| 0.0219 | 250 | 0.2562 | - |
| 0.0263 | 300 | 0.2623 | - |
| 0.0307 | 350 | 0.1904 | - |
| 0.0350 | 400 | 0.2314 | - |
| 0.0394 | 450 | 0.1669 | - |
| 0.0438 | 500 | 0.1135 | - |
| 0.0482 | 550 | 0.1489 | - |
| 0.0525 | 600 | 0.1907 | - |
| 0.0569 | 650 | 0.1728 | - |
| 0.0613 | 700 | 0.125 | - |
| 0.0657 | 750 | 0.109 | - |
| 0.0701 | 800 | 0.0968 | - |
| 0.0744 | 850 | 0.2101 | - |
| 0.0788 | 900 | 0.1974 | - |
| 0.0832 | 950 | 0.1986 | - |
| 0.0876 | 1000 | 0.0747 | - |
| 0.0920 | 1050 | 0.1117 | - |
| 0.0963 | 1100 | 0.1092 | - |
| 0.1007 | 1150 | 0.1582 | - |
| 0.1051 | 1200 | 0.1243 | - |
| 0.1095 | 1250 | 0.2873 | - |
| 0.1139 | 1300 | 0.2415 | - |
| 0.1182 | 1350 | 0.1264 | - |
| 0.1226 | 1400 | 0.127 | - |
| 0.1270 | 1450 | 0.1308 | - |
| 0.1314 | 1500 | 0.0669 | - |
| 0.1358 | 1550 | 0.1218 | - |
| 0.1401 | 1600 | 0.114 | - |
| 0.1445 | 1650 | 0.0612 | - |
| 0.1489 | 1700 | 0.0527 | - |
| 0.1533 | 1750 | 0.1421 | - |
| 0.1576 | 1800 | 0.0048 | - |
| 0.1620 | 1850 | 0.0141 | - |
| 0.1664 | 1900 | 0.0557 | - |
| 0.1708 | 1950 | 0.0206 | - |
| 0.1752 | 2000 | 0.1171 | - |
| 0.1795 | 2050 | 0.0968 | - |
| 0.1839 | 2100 | 0.0243 | - |
| 0.1883 | 2150 | 0.0233 | - |
| 0.1927 | 2200 | 0.0738 | - |
| 0.1971 | 2250 | 0.0071 | - |
| 0.2014 | 2300 | 0.0353 | - |
| 0.2058 | 2350 | 0.0602 | - |
| 0.2102 | 2400 | 0.003 | - |
| 0.2146 | 2450 | 0.0625 | - |
| 0.2190 | 2500 | 0.0173 | - |
| 0.2233 | 2550 | 0.1017 | - |
| 0.2277 | 2600 | 0.0582 | - |
| 0.2321 | 2650 | 0.0437 | - |
| 0.2365 | 2700 | 0.104 | - |
| 0.2408 | 2750 | 0.0156 | - |
| 0.2452 | 2800 | 0.0034 | - |
| 0.2496 | 2850 | 0.0343 | - |
| 0.2540 | 2900 | 0.1106 | - |
| 0.2584 | 2950 | 0.001 | - |
| 0.2627 | 3000 | 0.004 | - |
| 0.2671 | 3050 | 0.0074 | - |
| 0.2715 | 3100 | 0.0849 | - |
| 0.2759 | 3150 | 0.0009 | - |
| 0.2803 | 3200 | 0.0379 | - |
| 0.2846 | 3250 | 0.0109 | - |
| 0.2890 | 3300 | 0.0019 | - |
| 0.2934 | 3350 | 0.0154 | - |
| 0.2978 | 3400 | 0.0017 | - |
| 0.3022 | 3450 | 0.0003 | - |
| 0.3065 | 3500 | 0.0002 | - |
| 0.3109 | 3550 | 0.0025 | - |
| 0.3153 | 3600 | 0.0123 | - |
| 0.3197 | 3650 | 0.0007 | - |
| 0.3240 | 3700 | 0.0534 | - |
| 0.3284 | 3750 | 0.0004 | - |
| 0.3328 | 3800 | 0.0084 | - |
| 0.3372 | 3850 | 0.0088 | - |
| 0.3416 | 3900 | 0.0201 | - |
| 0.3459 | 3950 | 0.0002 | - |
| 0.3503 | 4000 | 0.0102 | - |
| 0.3547 | 4050 | 0.0043 | - |
| 0.3591 | 4100 | 0.0124 | - |
| 0.3635 | 4150 | 0.0845 | - |
| 0.3678 | 4200 | 0.0002 | - |
| 0.3722 | 4250 | 0.0014 | - |
| 0.3766 | 4300 | 0.1131 | - |
| 0.3810 | 4350 | 0.0612 | - |
| 0.3854 | 4400 | 0.0577 | - |
| 0.3897 | 4450 | 0.0235 | - |
| 0.3941 | 4500 | 0.0156 | - |
| 0.3985 | 4550 | 0.0078 | - |
| 0.4029 | 4600 | 0.0356 | - |
| 0.4073 | 4650 | 0.0595 | - |
| 0.4116 | 4700 | 0.0001 | - |
| 0.4160 | 4750 | 0.0018 | - |
| 0.4204 | 4800 | 0.0013 | - |
| 0.4248 | 4850 | 0.0008 | - |
| 0.4291 | 4900 | 0.0832 | - |
| 0.4335 | 4950 | 0.0083 | - |
| 0.4379 | 5000 | 0.0007 | - |
| 0.4423 | 5050 | 0.0417 | - |
| 0.4467 | 5100 | 0.0001 | - |
| 0.4510 | 5150 | 0.0218 | - |
| 0.4554 | 5200 | 0.0001 | - |
| 0.4598 | 5250 | 0.0012 | - |
| 0.4642 | 5300 | 0.0002 | - |
| 0.4686 | 5350 | 0.0006 | - |
| 0.4729 | 5400 | 0.0223 | - |
| 0.4773 | 5450 | 0.0612 | - |
| 0.4817 | 5500 | 0.0004 | - |
| 0.4861 | 5550 | 0.0 | - |
| 0.4905 | 5600 | 0.0007 | - |
| 0.4948 | 5650 | 0.0007 | - |
| 0.4992 | 5700 | 0.0116 | - |
| 0.5036 | 5750 | 0.0262 | - |
| 0.5080 | 5800 | 0.0336 | - |
| 0.5123 | 5850 | 0.026 | - |
| 0.5167 | 5900 | 0.0004 | - |
| 0.5211 | 5950 | 0.0001 | - |
| 0.5255 | 6000 | 0.0001 | - |
| 0.5299 | 6050 | 0.0001 | - |
| 0.5342 | 6100 | 0.0029 | - |
| 0.5386 | 6150 | 0.0001 | - |
| 0.5430 | 6200 | 0.0699 | - |
| 0.5474 | 6250 | 0.0262 | - |
| 0.5518 | 6300 | 0.0269 | - |
| 0.5561 | 6350 | 0.0002 | - |
| 0.5605 | 6400 | 0.0666 | - |
| 0.5649 | 6450 | 0.0209 | - |
| 0.5693 | 6500 | 0.0003 | - |
| 0.5737 | 6550 | 0.0001 | - |
| 0.5780 | 6600 | 0.0115 | - |
| 0.5824 | 6650 | 0.0003 | - |
| 0.5868 | 6700 | 0.0001 | - |
| 0.5912 | 6750 | 0.0056 | - |
| 0.5956 | 6800 | 0.0603 | - |
| 0.5999 | 6850 | 0.0002 | - |
| 0.6043 | 6900 | 0.0003 | - |
| 0.6087 | 6950 | 0.0092 | - |
| 0.6131 | 7000 | 0.0562 | - |
| 0.6174 | 7050 | 0.0408 | - |
| 0.6218 | 7100 | 0.0001 | - |
| 0.6262 | 7150 | 0.0035 | - |
| 0.6306 | 7200 | 0.0337 | - |
| 0.6350 | 7250 | 0.0024 | - |
| 0.6393 | 7300 | 0.0005 | - |
| 0.6437 | 7350 | 0.0001 | - |
| 0.6481 | 7400 | 0.0 | - |
| 0.6525 | 7450 | 0.0001 | - |
| 0.6569 | 7500 | 0.0002 | - |
| 0.6612 | 7550 | 0.0004 | - |
| 0.6656 | 7600 | 0.0125 | - |
| 0.6700 | 7650 | 0.0005 | - |
| 0.6744 | 7700 | 0.0157 | - |
| 0.6788 | 7750 | 0.0055 | - |
| 0.6831 | 7800 | 0.0 | - |
| 0.6875 | 7850 | 0.0053 | - |
| 0.6919 | 7900 | 0.0 | - |
| 0.6963 | 7950 | 0.0002 | - |
| 0.7006 | 8000 | 0.0002 | - |
| 0.7050 | 8050 | 0.0001 | - |
| 0.7094 | 8100 | 0.0001 | - |
| 0.7138 | 8150 | 0.0001 | - |
| 0.7182 | 8200 | 0.0007 | - |
| 0.7225 | 8250 | 0.0002 | - |
| 0.7269 | 8300 | 0.0001 | - |
| 0.7313 | 8350 | 0.0 | - |
| 0.7357 | 8400 | 0.0156 | - |
| 0.7401 | 8450 | 0.0098 | - |
| 0.7444 | 8500 | 0.0 | - |
| 0.7488 | 8550 | 0.0001 | - |
| 0.7532 | 8600 | 0.0042 | - |
| 0.7576 | 8650 | 0.0 | - |
| 0.7620 | 8700 | 0.0 | - |
| 0.7663 | 8750 | 0.0056 | - |
| 0.7707 | 8800 | 0.0 | - |
| 0.7751 | 8850 | 0.0 | - |
| 0.7795 | 8900 | 0.013 | - |
| 0.7839 | 8950 | 0.0 | - |
| 0.7882 | 9000 | 0.0001 | - |
| 0.7926 | 9050 | 0.0 | - |
| 0.7970 | 9100 | 0.0 | - |
| 0.8014 | 9150 | 0.0 | - |
| 0.8057 | 9200 | 0.0 | - |
| 0.8101 | 9250 | 0.0 | - |
| 0.8145 | 9300 | 0.0007 | - |
| 0.8189 | 9350 | 0.0 | - |
| 0.8233 | 9400 | 0.0002 | - |
| 0.8276 | 9450 | 0.0 | - |
| 0.8320 | 9500 | 0.0 | - |
| 0.8364 | 9550 | 0.0089 | - |
| 0.8408 | 9600 | 0.0001 | - |
| 0.8452 | 9650 | 0.0 | - |
| 0.8495 | 9700 | 0.0 | - |
| 0.8539 | 9750 | 0.0 | - |
| 0.8583 | 9800 | 0.0565 | - |
| 0.8627 | 9850 | 0.0161 | - |
| 0.8671 | 9900 | 0.0 | - |
| 0.8714 | 9950 | 0.0246 | - |
| 0.8758 | 10000 | 0.0 | - |
| 0.8802 | 10050 | 0.0 | - |
| 0.8846 | 10100 | 0.012 | - |
| 0.8889 | 10150 | 0.0 | - |
| 0.8933 | 10200 | 0.0 | - |
| 0.8977 | 10250 | 0.0 | - |
| 0.9021 | 10300 | 0.0 | - |
| 0.9065 | 10350 | 0.0 | - |
| 0.9108 | 10400 | 0.0 | - |
| 0.9152 | 10450 | 0.0 | - |
| 0.9196 | 10500 | 0.0 | - |
| 0.9240 | 10550 | 0.0023 | - |
| 0.9284 | 10600 | 0.0 | - |
| 0.9327 | 10650 | 0.0006 | - |
| 0.9371 | 10700 | 0.0 | - |
| 0.9415 | 10750 | 0.0 | - |
| 0.9459 | 10800 | 0.0 | - |
| 0.9503 | 10850 | 0.0 | - |
| 0.9546 | 10900 | 0.0 | - |
| 0.9590 | 10950 | 0.0243 | - |
| 0.9634 | 11000 | 0.0107 | - |
| 0.9678 | 11050 | 0.0001 | - |
| 0.9721 | 11100 | 0.0 | - |
| 0.9765 | 11150 | 0.0 | - |
| 0.9809 | 11200 | 0.0274 | - |
| 0.9853 | 11250 | 0.0 | - |
| 0.9897 | 11300 | 0.0 | - |
| 0.9940 | 11350 | 0.0 | - |
| 0.9984 | 11400 | 0.0 | - |
| 0.0007 | 1 | 0.2021 | - |
| 0.0329 | 50 | 0.1003 | - |
| 0.0657 | 100 | 0.2282 | - |
| 0.0986 | 150 | 0.0507 | - |
| 0.1314 | 200 | 0.046 | - |
| 0.1643 | 250 | 0.0001 | - |
| 0.1971 | 300 | 0.0495 | - |
| 0.2300 | 350 | 0.0031 | - |
| 0.2628 | 400 | 0.0004 | - |
| 0.2957 | 450 | 0.0002 | - |
| 0.3285 | 500 | 0.0 | - |
| 0.3614 | 550 | 0.0 | - |
| 0.3942 | 600 | 0.0 | - |
| 0.4271 | 650 | 0.0001 | - |
| 0.4599 | 700 | 0.0 | - |
| 0.4928 | 750 | 0.0 | - |
| 0.5256 | 800 | 0.0 | - |
| 0.5585 | 850 | 0.0 | - |
| 0.5913 | 900 | 0.0001 | - |
| 0.6242 | 950 | 0.0 | - |
| 0.6570 | 1000 | 0.0001 | - |
| 0.6899 | 1050 | 0.0 | - |
| 0.7227 | 1100 | 0.0 | - |
| 0.7556 | 1150 | 0.0 | - |
| 0.7884 | 1200 | 0.0 | - |
| 0.8213 | 1250 | 0.0 | - |
| 0.8541 | 1300 | 0.0 | - |
| 0.8870 | 1350 | 0.0 | - |
| 0.9198 | 1400 | 0.0 | - |
| 0.9527 | 1450 | 0.0001 | - |
| 0.9855 | 1500 | 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}
}
```