|
--- |
|
library_name: setfit |
|
tags: |
|
- setfit |
|
- sentence-transformers |
|
- text-classification |
|
- generated_from_setfit_trainer |
|
metrics: |
|
- accuracy |
|
widget: |
|
- text: 'It was a jihad training camp. |
|
|
|
' |
|
- text: 'Batten echoed that sentiment saying, “Tommy Robinson is a political prisoner." |
|
|
|
' |
|
- text: 'Failing to answer, Ellison tried to move from person to person, allowing |
|
his minions to try and provide cover for him, similar to that of Maxine Waters, |
|
but there was no "member''s only" elevator to flee into. |
|
|
|
' |
|
- text: 'More details about the horrid compound could be revealed Wednesday when the |
|
five adults arrested from the site make their first court appearances. |
|
|
|
' |
|
- text: 'Black Death Warning: The Plague Is Impossible To Eradicate |
|
|
|
' |
|
pipeline_tag: text-classification |
|
inference: false |
|
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: 0.5849056603773585 |
|
name: Accuracy |
|
--- |
|
|
|
# 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 OneVsRestClassifier 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 OneVsRestClassifier instance |
|
- **Maximum Sequence Length:** 512 tokens |
|
<!-- - **Number of Classes:** Unknown --> |
|
<!-- - **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) |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
| Label | Accuracy | |
|
|:--------|:---------| |
|
| **all** | 0.5849 | |
|
|
|
## 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("anismahmahi/G2-multilabel-setfit-model") |
|
# Run inference |
|
preds = model("It was a jihad training camp. |
|
") |
|
``` |
|
|
|
<!-- |
|
### 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 | 26.6518 | 129 | |
|
|
|
### Training Hyperparameters |
|
- batch_size: (16, 16) |
|
- num_epochs: (2, 2) |
|
- max_steps: -1 |
|
- sampling_strategy: oversampling |
|
- num_iterations: 10 |
|
- 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 |
|
- eval_max_steps: -1 |
|
- load_best_model_at_end: True |
|
|
|
### Training Results |
|
| Epoch | Step | Training Loss | Validation Loss | |
|
|:-------:|:--------:|:-------------:|:---------------:| |
|
| 0.0006 | 1 | 0.3905 | - | |
|
| 0.0275 | 50 | 0.2239 | - | |
|
| 0.0550 | 100 | 0.2359 | - | |
|
| 0.0826 | 150 | 0.2443 | - | |
|
| 0.1101 | 200 | 0.2495 | - | |
|
| 0.1376 | 250 | 0.2498 | - | |
|
| 0.1651 | 300 | 0.116 | - | |
|
| 0.1926 | 350 | 0.1672 | - | |
|
| 0.2201 | 400 | 0.1281 | - | |
|
| 0.2477 | 450 | 0.139 | - | |
|
| 0.2752 | 500 | 0.0615 | - | |
|
| 0.3027 | 550 | 0.0972 | - | |
|
| 0.3302 | 600 | 0.0851 | - | |
|
| 0.3577 | 650 | 0.1769 | - | |
|
| 0.3853 | 700 | 0.1673 | - | |
|
| 0.4128 | 750 | 0.0615 | - | |
|
| 0.4403 | 800 | 0.1232 | - | |
|
| 0.4678 | 850 | 0.0094 | - | |
|
| 0.4953 | 900 | 0.0135 | - | |
|
| 0.5228 | 950 | 0.0107 | - | |
|
| 0.5504 | 1000 | 0.1137 | - | |
|
| 0.5779 | 1050 | 0.0173 | - | |
|
| 0.6054 | 1100 | 0.0573 | - | |
|
| 0.6329 | 1150 | 0.0115 | - | |
|
| 0.6604 | 1200 | 0.0374 | - | |
|
| 0.6879 | 1250 | 0.0231 | - | |
|
| 0.7155 | 1300 | 0.0392 | - | |
|
| 0.7430 | 1350 | 0.0754 | - | |
|
| 0.7705 | 1400 | 0.007 | - | |
|
| 0.7980 | 1450 | 0.0138 | - | |
|
| 0.8255 | 1500 | 0.0569 | - | |
|
| 0.8531 | 1550 | 0.0971 | - | |
|
| 0.8806 | 1600 | 0.1052 | - | |
|
| 0.9081 | 1650 | 0.0084 | - | |
|
| 0.9356 | 1700 | 0.0859 | - | |
|
| 0.9631 | 1750 | 0.0081 | - | |
|
| 0.9906 | 1800 | 0.0362 | - | |
|
| 1.0 | 1817 | - | 0.2354 | |
|
| 1.0182 | 1850 | 0.0429 | - | |
|
| 1.0457 | 1900 | 0.056 | - | |
|
| 1.0732 | 1950 | 0.0098 | - | |
|
| 1.1007 | 2000 | 0.002 | - | |
|
| 1.1282 | 2050 | 0.0892 | - | |
|
| 1.1558 | 2100 | 0.0557 | - | |
|
| 1.1833 | 2150 | 0.001 | - | |
|
| 1.2108 | 2200 | 0.0125 | - | |
|
| 1.2383 | 2250 | 0.0152 | - | |
|
| 1.2658 | 2300 | 0.0202 | - | |
|
| 1.2933 | 2350 | 0.0593 | - | |
|
| 1.3209 | 2400 | 0.007 | - | |
|
| 1.3484 | 2450 | 0.014 | - | |
|
| 1.3759 | 2500 | 0.003 | - | |
|
| 1.4034 | 2550 | 0.0012 | - | |
|
| 1.4309 | 2600 | 0.0139 | - | |
|
| 1.4584 | 2650 | 0.0149 | - | |
|
| 1.4860 | 2700 | 0.002 | - | |
|
| 1.5135 | 2750 | 0.009 | - | |
|
| 1.5410 | 2800 | 0.0066 | - | |
|
| 1.5685 | 2850 | 0.0173 | - | |
|
| 1.5960 | 2900 | 0.0052 | - | |
|
| 1.6236 | 2950 | 0.0039 | - | |
|
| 1.6511 | 3000 | 0.0042 | - | |
|
| 1.6786 | 3050 | 0.0339 | - | |
|
| 1.7061 | 3100 | 0.001 | - | |
|
| 1.7336 | 3150 | 0.0005 | - | |
|
| 1.7611 | 3200 | 0.0049 | - | |
|
| 1.7887 | 3250 | 0.01 | - | |
|
| 1.8162 | 3300 | 0.0815 | - | |
|
| 1.8437 | 3350 | 0.0227 | - | |
|
| 1.8712 | 3400 | 0.005 | - | |
|
| 1.8987 | 3450 | 0.0053 | - | |
|
| 1.9263 | 3500 | 0.0152 | - | |
|
| 1.9538 | 3550 | 0.0155 | - | |
|
| 1.9813 | 3600 | 0.0182 | - | |
|
| **2.0** | **3634** | **-** | **0.2266** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
### 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: 2.16.1 |
|
- 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## 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.* |
|
--> |
