|
|
--- |
|
|
tags: |
|
|
- setfit |
|
|
- sentence-transformers |
|
|
- text-classification |
|
|
- generated_from_setfit_trainer |
|
|
widget: |
|
|
- text: it does not make sense because sally believe its makes sense and at the same |
|
|
time does not make sense to help the homeless. |
|
|
- text: it contradicts itself- how can something be right and you then think it's |
|
|
not right? |
|
|
- text: it made sense because it is tom's opinion that cyberbullying is not wrong. |
|
|
- text: a person can think it is raining even when it is. there is nothing wrong with |
|
|
thinking that way. the thought makes sense even though the fact is incorrect. |
|
|
- text: they contradict their own opinions on the morals. although i can understand |
|
|
how they came to that conclusion. perhaps they mean, helping the homeless is morally |
|
|
right, however it's not right for my situation. context and clarification is key |
|
|
here. |
|
|
metrics: |
|
|
- accuracy |
|
|
- precision |
|
|
- recall |
|
|
- f1 |
|
|
pipeline_tag: text-classification |
|
|
library_name: setfit |
|
|
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: 0.9210526315789473 |
|
|
name: Accuracy |
|
|
- type: precision |
|
|
value: 0.9198717948717949 |
|
|
name: Precision |
|
|
- type: recall |
|
|
value: 0.9030769230769231 |
|
|
name: Recall |
|
|
- type: f1 |
|
|
value: 0.9105882352941177 |
|
|
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:** 3 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 | |
|
|
|:-----------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
|
| Enrichment / reinterpretation | <ul><li>'the statement recognised the objective compassion but the opinion contradicted it'</li><li>"the person's individual belief doesn't tally with the accepted belief; this is perfectly reasonable."</li><li>'cyberbully may seem cruel to everyone, but to tom, he does not feel cruel to him.'</li></ul> | |
|
|
| Linguistic (in)felicity | <ul><li>'because if its wrong how can you then make a statement saying it is not wrong'</li><li>'it is contradictory.'</li><li>'because the writer just stated that it s raining so how could she then not know if it is raining?'</li></ul> | |
|
|
| Lack of understanding / clear misunderstanding | <ul><li>'it sounds very contradictory'</li><li>'it reads well and makes sense'</li><li>'it make not sense on one hand help the homeless people is right, on the hand hand it is not unethical.'</li></ul> | |
|
|
|
|
|
## Evaluation |
|
|
|
|
|
### Metrics |
|
|
| Label | Accuracy | Precision | Recall | F1 | |
|
|
|:--------|:---------|:----------|:-------|:-------| |
|
|
| **all** | 0.9211 | 0.9199 | 0.9031 | 0.9106 | |
|
|
|
|
|
## 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 made sense because it is tom's opinion that cyberbullying is not wrong.") |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
### 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 | 2 | 16.375 | 92 | |
|
|
|
|
|
| Label | Training Sample Count | |
|
|
|:-----------------------------------------------|:----------------------| |
|
|
| Enrichment / reinterpretation | 29 | |
|
|
| Lack of understanding / clear misunderstanding | 11 | |
|
|
| Linguistic (in)felicity | 112 | |
|
|
|
|
|
### 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: 376 |
|
|
- eval_max_steps: -1 |
|
|
- load_best_model_at_end: False |
|
|
|
|
|
### Training Results |
|
|
| Epoch | Step | Training Loss | Validation Loss | |
|
|
|:------:|:----:|:-------------:|:---------------:| |
|
|
| 0.0026 | 1 | 0.2512 | - | |
|
|
| 0.1316 | 50 | 0.2213 | - | |
|
|
| 0.2632 | 100 | 0.1707 | - | |
|
|
| 0.3947 | 150 | 0.0839 | - | |
|
|
| 0.5263 | 200 | 0.0335 | - | |
|
|
| 0.6579 | 250 | 0.0141 | - | |
|
|
| 0.7895 | 300 | 0.0072 | - | |
|
|
| 0.9211 | 350 | 0.0026 | - | |
|
|
| 1.0526 | 400 | 0.0008 | - | |
|
|
| 1.1842 | 450 | 0.0006 | - | |
|
|
| 1.3158 | 500 | 0.0004 | - | |
|
|
| 1.4474 | 550 | 0.0002 | - | |
|
|
| 1.5789 | 600 | 0.0002 | - | |
|
|
| 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 | 0.0001 | - | |
|
|
| 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 | 0.0002 | - | |
|
|
| 4.2105 | 1600 | 0.0001 | - | |
|
|
| 4.3421 | 1650 | 0.0033 | - | |
|
|
| 4.4737 | 1700 | 0.0001 | - | |
|
|
| 4.6053 | 1750 | 0.0004 | - | |
|
|
| 4.7368 | 1800 | 0.0035 | - | |
|
|
| 4.8684 | 1850 | 0.0002 | - | |
|
|
| 5.0 | 1900 | 0.0003 | - | |
|
|
| 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 | 0.0 | - | |
|
|
| 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 | 0.0001 | - | |
|
|
| 9.0789 | 3450 | 0.0001 | - | |
|
|
| 9.2105 | 3500 | 0.0001 | - | |
|
|
| 9.3421 | 3550 | 0.0 | - | |
|
|
| 9.4737 | 3600 | 0.0 | - | |
|
|
| 9.6053 | 3650 | 0.0001 | - | |
|
|
| 9.7368 | 3700 | 0.0001 | - | |
|
|
| 9.8684 | 3750 | 0.0 | - | |
|
|
| 10.0 | 3800 | 0.0 | - | |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.9 |
|
|
- SetFit: 1.1.2 |
|
|
- Sentence Transformers: 4.1.0 |
|
|
- Transformers: 4.52.4 |
|
|
- PyTorch: 2.7.1 |
|
|
- Datasets: 3.6.0 |
|
|
- Tokenizers: 0.21.1 |
|
|
|
|
|
## 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.* |
|
|
--> |
