SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- 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-multilingual-MiniLM-L12-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 128 tokens
- Number of Classes: 3 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
Label | Examples |
---|---|
0 |
|
1 |
|
2 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.79 |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("ehsanhallo/setfit-paraphrase-multilingual-MiniLM-L12-v2-ig-fa")
# Run inference
preds = model(" where`d you go!")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 6.4184 | 75 |
Label | Training Sample Count |
---|---|
0 | 69 |
1 | 238 |
2 | 551 |
Training Hyperparameters
- batch_size: (32, 16)
- num_epochs: (1, 2)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 5e-06)
- head_learning_rate: 0.002
- 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: 42
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0001 | 1 | 0.1767 | - |
0.0216 | 250 | 0.1513 | - |
0.0431 | 500 | 0.0629 | 0.2389 |
0.0647 | 750 | 0.0351 | - |
0.0862 | 1000 | 0.0015 | 0.1886 |
0.1078 | 1250 | 0.0003 | - |
0.1293 | 1500 | 0.0004 | 0.1813 |
0.1509 | 1750 | 0.0002 | - |
0.1724 | 2000 | 0.0002 | 0.1807 |
0.1940 | 2250 | 0.0001 | - |
0.2155 | 2500 | 0.0001 | 0.187 |
0.2371 | 2750 | 0.0001 | - |
0.2586 | 3000 | 0.0001 | 0.1903 |
0.2802 | 3250 | 0.0001 | - |
0.3018 | 3500 | 0.0 | 0.1864 |
0.3233 | 3750 | 0.0 | - |
0.3449 | 4000 | 0.0 | 0.193 |
0.3664 | 4250 | 0.0 | - |
0.3880 | 4500 | 0.0 | 0.1879 |
0.4095 | 4750 | 0.0 | - |
0.4311 | 5000 | 0.0 | 0.1887 |
0.4526 | 5250 | 0.0 | - |
0.4742 | 5500 | 0.0 | 0.187 |
0.4957 | 5750 | 0.0 | - |
0.5173 | 6000 | 0.0001 | 0.205 |
0.5388 | 6250 | 0.0 | - |
0.5604 | 6500 | 0.0 | 0.205 |
0.5819 | 6750 | 0.0 | - |
0.6035 | 7000 | 0.0 | 0.2018 |
0.6251 | 7250 | 0.0 | - |
0.6466 | 7500 | 0.0 | 0.2022 |
0.6682 | 7750 | 0.0 | - |
0.6897 | 8000 | 0.0 | 0.2063 |
0.7113 | 8250 | 0.0 | - |
0.7328 | 8500 | 0.0 | 0.2143 |
0.7544 | 8750 | 0.0 | - |
0.7759 | 9000 | 0.0 | 0.2206 |
0.7975 | 9250 | 0.0 | - |
0.8190 | 9500 | 0.0 | 0.2167 |
0.8406 | 9750 | 0.0 | - |
0.8621 | 10000 | 0.0 | 0.2176 |
0.8837 | 10250 | 0.0 | - |
0.9053 | 10500 | 0.0 | 0.217 |
0.9268 | 10750 | 0.0 | - |
0.9484 | 11000 | 0.0 | 0.2153 |
0.9699 | 11250 | 0.0 | - |
0.9915 | 11500 | 0.0 | 0.2137 |
- 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
@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}
}
- Downloads last month
- 29
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.