---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: if it is raining, as was stated, then it is irrelevant what someone thinks
abut whether or not it is raining. it is raining. therefore, the statement was
nonsensical.
- text: the first part of the sentence was a fact but the second half was sally's
opinion
- text: because on one hand it is but actually not a long term solution
- text: it contradicted itself
- text: cyberbully may seem cruel to everyone, but to tom, he does not feel cruel
to him.
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: text-classification
library_name: setfit
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: 0.868421052631579
name: Accuracy
- type: precision
value: 0.5642857142857144
name: Precision
- type: recall
value: 0.5629370629370629
name: Recall
- type: f1
value: 0.562610229276896
name: F1
---
# 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:** 3 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 |
|:-----------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Linguistic (in)felicity | - 'because the second statement negates what was stated in the first part of the sentence'
- 'there is a logic conflict in the statement that renders it bizarre and nonsensical.'
- 'there was a contradiction of statements if read at face value, however, it could be read that being homeless is not right in which case the statement would make sense. it is unclear.'
|
| Enrichment / reinterpretation | - 'the statement recognised the objective compassion but the opinion contradicted it'
- "because while it is compassionate to help the homeless people don't always do it out of compassion."
- 'it could be the way how homeless are helped. there could be better ways to handle that'
|
| Lack of understanding / clear misunderstanding | - 'it simply sounded stupid. i doubt it makes any sense'
- 'it statement didnt make any sense, for us to better understand, tom needs to further explain his reason for stating why its not cruel after first saying it is'
- 'it sounds very contradictory'
|
## Evaluation
### Metrics
| Label | Accuracy | Precision | Recall | F1 |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.8684 | 0.5643 | 0.5629 | 0.5626 |
## 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("it contradicted itself")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 2 | 16.6447 | 92 |
| Label | Training Sample Count |
|:-----------------------------------------------|:----------------------|
| Enrichment / reinterpretation | 31 |
| Lack of understanding / clear misunderstanding | 10 |
| Linguistic (in)felicity | 111 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (10, 10)
- 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
- l2_weight: 0.01
- seed: 3786
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0026 | 1 | 0.2539 | - |
| 0.1316 | 50 | 0.2248 | - |
| 0.2632 | 100 | 0.1681 | - |
| 0.3947 | 150 | 0.0854 | - |
| 0.5263 | 200 | 0.0128 | - |
| 0.6579 | 250 | 0.0074 | - |
| 0.7895 | 300 | 0.0017 | - |
| 0.9211 | 350 | 0.0021 | - |
| 1.0526 | 400 | 0.0024 | - |
| 1.1842 | 450 | 0.0004 | - |
| 1.3158 | 500 | 0.0011 | - |
| 1.4474 | 550 | 0.0016 | - |
| 1.5789 | 600 | 0.0003 | - |
| 1.7105 | 650 | 0.0002 | - |
| 1.8421 | 700 | 0.0002 | - |
| 1.9737 | 750 | 0.0002 | - |
| 2.1053 | 800 | 0.0002 | - |
| 2.2368 | 850 | 0.0002 | - |
| 2.3684 | 900 | 0.0002 | - |
| 2.5 | 950 | 0.0001 | - |
| 2.6316 | 1000 | 0.0001 | - |
| 2.7632 | 1050 | 0.0001 | - |
| 2.8947 | 1100 | 0.0001 | - |
| 3.0263 | 1150 | 0.0001 | - |
| 3.1579 | 1200 | 0.0001 | - |
| 3.2895 | 1250 | 0.0001 | - |
| 3.4211 | 1300 | 0.0001 | - |
| 3.5526 | 1350 | 0.0001 | - |
| 3.6842 | 1400 | 0.0001 | - |
| 3.8158 | 1450 | 0.0001 | - |
| 3.9474 | 1500 | 0.0001 | - |
| 4.0789 | 1550 | 0.0001 | - |
| 4.2105 | 1600 | 0.0001 | - |
| 4.3421 | 1650 | 0.0001 | - |
| 4.4737 | 1700 | 0.0001 | - |
| 4.6053 | 1750 | 0.0001 | - |
| 4.7368 | 1800 | 0.0001 | - |
| 4.8684 | 1850 | 0.0001 | - |
| 5.0 | 1900 | 0.0001 | - |
| 5.1316 | 1950 | 0.0001 | - |
| 5.2632 | 2000 | 0.0001 | - |
| 5.3947 | 2050 | 0.0001 | - |
| 5.5263 | 2100 | 0.0001 | - |
| 5.6579 | 2150 | 0.0001 | - |
| 5.7895 | 2200 | 0.0001 | - |
| 5.9211 | 2250 | 0.0001 | - |
| 6.0526 | 2300 | 0.0001 | - |
| 6.1842 | 2350 | 0.0001 | - |
| 6.3158 | 2400 | 0.0001 | - |
| 6.4474 | 2450 | 0.0001 | - |
| 6.5789 | 2500 | 0.0001 | - |
| 6.7105 | 2550 | 0.0001 | - |
| 6.8421 | 2600 | 0.0001 | - |
| 6.9737 | 2650 | 0.0001 | - |
| 7.1053 | 2700 | 0.0001 | - |
| 7.2368 | 2750 | 0.0001 | - |
| 7.3684 | 2800 | 0.0001 | - |
| 7.5 | 2850 | 0.0001 | - |
| 7.6316 | 2900 | 0.0001 | - |
| 7.7632 | 2950 | 0.0001 | - |
| 7.8947 | 3000 | 0.0001 | - |
| 8.0263 | 3050 | 0.0001 | - |
| 8.1579 | 3100 | 0.0001 | - |
| 8.2895 | 3150 | 0.0001 | - |
| 8.4211 | 3200 | 0.0001 | - |
| 8.5526 | 3250 | 0.0001 | - |
| 8.6842 | 3300 | 0.0001 | - |
| 8.8158 | 3350 | 0.0001 | - |
| 8.9474 | 3400 | 0.0012 | - |
| 9.0789 | 3450 | 0.0003 | - |
| 9.2105 | 3500 | 0.0001 | - |
| 9.3421 | 3550 | 0.0001 | - |
| 9.4737 | 3600 | 0.0001 | - |
| 9.6053 | 3650 | 0.0001 | - |
| 9.7368 | 3700 | 0.0001 | - |
| 9.8684 | 3750 | 0.0001 | - |
| 10.0 | 3800 | 0.0 | - |
### Framework Versions
- Python: 3.11.9
- SetFit: 1.1.3
- Sentence Transformers: 5.1.0
- Transformers: 4.55.2
- PyTorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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}
}
```