SetFit with akhooli/sbert_ar_nli_500k_ubc_norm
This is a SetFit model that can be used for Text Classification. This SetFit model uses akhooli/sbert_ar_nli_500k_ubc_norm 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: akhooli/sbert_ar_nli_500k_ubc_norm
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 2 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 |
---|---|
positive |
|
negative |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.8398 |
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("akhooli/setfit_ar_ubc_hs")
# Run inference
preds = model("شيوعي
علماني
مسيحي
انصار سنه
صوفي
يمثلك التجمع
لا يمثلك التجمع
اهلا بكم جميعا فنحن نريد بناء وطن ❤")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 18.8448 | 185 |
Label | Training Sample Count |
---|---|
negative | 5200 |
positive | 4943 |
Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- max_steps: 6000
- sampling_strategy: undersampling
- 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
- l2_weight: 0.01
- seed: 42
- run_name: setfit_hate_52k_ubc_6k
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0003 | 1 | 0.297 | - |
0.0333 | 100 | 0.2741 | - |
0.0667 | 200 | 0.2178 | - |
0.1 | 300 | 0.1724 | - |
0.1333 | 400 | 0.1449 | - |
0.1667 | 500 | 0.1137 | - |
0.2 | 600 | 0.0902 | - |
0.2333 | 700 | 0.0708 | - |
0.2667 | 800 | 0.0535 | - |
0.3 | 900 | 0.0483 | - |
0.3333 | 1000 | 0.0386 | - |
0.3667 | 1100 | 0.0319 | - |
0.4 | 1200 | 0.0279 | - |
0.4333 | 1300 | 0.0201 | - |
0.4667 | 1400 | 0.0234 | - |
0.5 | 1500 | 0.0151 | - |
0.5333 | 1600 | 0.0151 | - |
0.5667 | 1700 | 0.0137 | - |
0.6 | 1800 | 0.0117 | - |
0.6333 | 1900 | 0.011 | - |
0.6667 | 2000 | 0.0097 | - |
0.7 | 2100 | 0.0077 | - |
0.7333 | 2200 | 0.0089 | - |
0.7667 | 2300 | 0.0069 | - |
0.8 | 2400 | 0.0064 | - |
0.8333 | 2500 | 0.0083 | - |
0.8667 | 2600 | 0.0061 | - |
0.9 | 2700 | 0.0063 | - |
0.9333 | 2800 | 0.0051 | - |
0.9667 | 2900 | 0.0047 | - |
1.0 | 3000 | 0.0044 | - |
1.0333 | 3100 | 0.0035 | - |
1.0667 | 3200 | 0.0034 | - |
1.1 | 3300 | 0.0035 | - |
1.1333 | 3400 | 0.0043 | - |
1.1667 | 3500 | 0.0035 | - |
1.2 | 3600 | 0.0024 | - |
1.2333 | 3700 | 0.003 | - |
1.2667 | 3800 | 0.002 | - |
1.3 | 3900 | 0.0029 | - |
1.3333 | 4000 | 0.003 | - |
1.3667 | 4100 | 0.002 | - |
1.4 | 4200 | 0.0022 | - |
1.4333 | 4300 | 0.0027 | - |
1.4667 | 4400 | 0.004 | - |
1.5 | 4500 | 0.001 | - |
1.5333 | 4600 | 0.0027 | - |
1.5667 | 4700 | 0.0027 | - |
1.6 | 4800 | 0.0014 | - |
1.6333 | 4900 | 0.0022 | - |
1.6667 | 5000 | 0.0027 | - |
1.7 | 5100 | 0.0018 | - |
1.7333 | 5200 | 0.0018 | - |
1.7667 | 5300 | 0.0012 | - |
1.8 | 5400 | 0.0014 | - |
1.8333 | 5500 | 0.0015 | - |
1.8667 | 5600 | 0.0009 | - |
1.9 | 5700 | 0.0012 | - |
1.9333 | 5800 | 0.0009 | - |
1.9667 | 5900 | 0.001 | - |
2.0 | 6000 | 0.0007 | - |
Framework Versions
- Python: 3.10.14
- SetFit: 1.2.0.dev0
- Sentence Transformers: 3.3.0
- Transformers: 4.45.1
- PyTorch: 2.4.0
- Datasets: 3.0.1
- Tokenizers: 0.20.0
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}
}
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