|
--- |
|
library_name: setfit |
|
tags: |
|
- setfit |
|
- sentence-transformers |
|
- text-classification |
|
- generated_from_setfit_trainer |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
widget: |
|
- text: GMB Gambia |
|
- text: ' end flyout 2 ' |
|
- text: 'Books |
|
|
|
' |
|
- text: Persistent |
|
- text: Session |
|
pipeline_tag: text-classification |
|
inference: true |
|
model-index: |
|
- name: SetFit |
|
results: |
|
- task: |
|
type: text-classification |
|
name: Text Classification |
|
dataset: |
|
name: Unknown |
|
type: unknown |
|
split: test |
|
metrics: |
|
- type: accuracy |
|
value: 0.87325 |
|
name: Accuracy |
|
- type: precision |
|
value: 0.8566450970632156 |
|
name: Precision |
|
- type: recall |
|
value: 0.8871134020618556 |
|
name: Recall |
|
- type: f1 |
|
value: 0.8716130665991391 |
|
name: F1 |
|
--- |
|
|
|
# SetFit |
|
|
|
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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:** [Unknown](https://huggingface.co/unknown) --> |
|
- **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:** 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 | |
|
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| True | <ul><li>'-. Pepsi-Colacold beats any cola cold! '</li><li>"Use “Jemes! et : L lemen peeple wen't Lemon. “i720 ait? "</li><li>'Ifit happens once, it could happen again. soptacaceee tates | WOE ¥ 1800 774 5025. '</li></ul> | |
|
| False | <ul><li>'ps-script'</li><li>'Make your bidder browser agnostic to access high-performing cookie alternative supply'</li><li>'International Students & Scholars'</li></ul> | |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
| Label | Accuracy | Precision | Recall | F1 | |
|
|:--------|:---------|:----------|:-------|:-------| |
|
| **all** | 0.8732 | 0.8566 | 0.8871 | 0.8716 | |
|
|
|
## 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("Books |
|
") |
|
``` |
|
|
|
<!-- |
|
### Downstream Use |
|
|
|
*List how someone could finetune this model on their own dataset.* |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Set Metrics |
|
| Training set | Min | Median | Max | |
|
|:-------------|:----|:-------|:-----| |
|
| Word count | 1 | 8.4845 | 1060 | |
|
|
|
| Label | Training Sample Count | |
|
|:------|:----------------------| |
|
| False | 7940 | |
|
| True | 8060 | |
|
|
|
### Framework Versions |
|
- Python: 3.11.0 |
|
- SetFit: 1.0.3 |
|
- Sentence Transformers: 2.3.0 |
|
- Transformers: 4.37.2 |
|
- PyTorch: 2.2.1+cu121 |
|
- Datasets: 2.16.1 |
|
- Tokenizers: 0.15.1 |
|
|
|
## 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
