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---
language:
- en
license: apache-2.0
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- sst2
metrics:
- precision
- recall
- f1
widget:
- text: 'this is a story of two misfits who do n''t stand a chance alone , but together
they are magnificent . '
- text: 'it does n''t believe in itself , it has no sense of humor ... it ''s just
plain bored . '
- text: 'the band ''s courage in the face of official repression is inspiring , especially
for aging hippies ( this one included ) . '
- text: 'a fast , funny , highly enjoyable movie . '
- text: 'the movie achieves as great an impact by keeping these thoughts hidden as
... ( quills ) did by showing them . '
pipeline_tag: text-classification
co2_eq_emissions:
emissions: 2.768308759172054
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.072
hardware_used: 1 x NVIDIA GeForce RTX 3090
base_model: sentence-transformers/all-MiniLM-L6-v2
model-index:
- name: SetFit with sentence-transformers/all-MiniLM-L6-v2 on sst2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: sst2
type: sst2
split: test
metrics:
- type: accuracy
value: 0.7512953367875648
name: Accuracy
---
# SetFit with sentence-transformers/all-MiniLM-L6-v2 on sst2
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [sst2](https://huggingface.co/datasets/sst2) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 2 classes
- **Training Dataset:** [sst2](https://huggingface.co/datasets/sst2)
- **Language:** en
- **License:** apache-2.0
### 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 |
|:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negative | <ul><li>'a tough pill to swallow and '</li><li>'indignation '</li><li>'that the typical hollywood disregard for historical truth and realism is at work here '</li></ul> |
| positive | <ul><li>"a moving experience for people who have n't read the book "</li><li>'in the best possible senses of both those words '</li><li>'to serve the work especially well '</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.7513 |
## 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 🤗 Hub
model = SetFitModel.from_pretrained("tomaarsen/setfit-all-MiniLM-L6-v2-sst2-8-shot")
# Run inference
preds = model("a fast , funny , highly enjoyable movie . ")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 2 | 10.2812 | 36 |
| Label | Training Sample Count |
|:---------|:----------------------|
| negative | 32 |
| positive | 32 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- 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
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:----------:|:------:|:-------------:|:---------------:|
| 0.0076 | 1 | 0.3787 | - |
| 0.0758 | 10 | 0.2855 | - |
| 0.1515 | 20 | 0.3458 | 0.29 |
| 0.2273 | 30 | 0.2496 | - |
| 0.3030 | 40 | 0.2398 | 0.2482 |
| 0.3788 | 50 | 0.2068 | - |
| 0.4545 | 60 | 0.2471 | 0.244 |
| 0.5303 | 70 | 0.2053 | - |
| **0.6061** | **80** | **0.1802** | **0.2361** |
| 0.6818 | 90 | 0.0767 | - |
| 0.7576 | 100 | 0.0279 | 0.2365 |
| 0.8333 | 110 | 0.0192 | - |
| 0.9091 | 120 | 0.0095 | 0.2527 |
| 0.9848 | 130 | 0.0076 | - |
| 1.0606 | 140 | 0.0082 | 0.2651 |
| 1.1364 | 150 | 0.0068 | - |
| 1.2121 | 160 | 0.0052 | 0.2722 |
| 1.2879 | 170 | 0.0029 | - |
| 1.3636 | 180 | 0.0042 | 0.273 |
| 1.4394 | 190 | 0.0026 | - |
| 1.5152 | 200 | 0.0036 | 0.2761 |
| 1.5909 | 210 | 0.0044 | - |
| 1.6667 | 220 | 0.0027 | 0.2796 |
| 1.7424 | 230 | 0.0025 | - |
| 1.8182 | 240 | 0.0025 | 0.2817 |
| 1.8939 | 250 | 0.003 | - |
| 1.9697 | 260 | 0.0026 | 0.2817 |
| 2.0455 | 270 | 0.0035 | - |
| 2.1212 | 280 | 0.002 | 0.2816 |
| 2.1970 | 290 | 0.0023 | - |
| 2.2727 | 300 | 0.0016 | 0.2821 |
| 2.3485 | 310 | 0.0023 | - |
| 2.4242 | 320 | 0.0015 | 0.2838 |
| 2.5 | 330 | 0.0014 | - |
| 2.5758 | 340 | 0.002 | 0.2842 |
| 2.6515 | 350 | 0.002 | - |
| 2.7273 | 360 | 0.0013 | 0.2847 |
| 2.8030 | 370 | 0.0009 | - |
| 2.8788 | 380 | 0.0018 | 0.2857 |
| 2.9545 | 390 | 0.0016 | - |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.003 kg of CO2
- **Hours Used**: 0.072 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.9.16
- SetFit: 1.0.0.dev0
- Sentence Transformers: 2.2.2
- Transformers: 4.29.0
- PyTorch: 1.13.1+cu117
- Datasets: 2.15.0
- Tokenizers: 0.13.3
## 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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