SetFit with intfloat/multilingual-e5-large
This is a SetFit model that can be used for Text Classification. This SetFit model uses intfloat/multilingual-e5-large 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: intfloat/multilingual-e5-large
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 12 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 | 
|---|---|
| 6 | 
 | 
| 2 | 
 | 
| 0 | 
 | 
| 10 | 
 | 
| 5 | 
 | 
| 7 | 
 | 
| 9 | 
 | 
| 11 | 
 | 
| 8 | 
 | 
| 3 | 
 | 
| 1 | 
 | 
| 4 | 
 | 
Evaluation
Metrics
| Label | Accuracy | 
|---|---|
| all | 0.25 | 
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 ๐ค Hub
model = SetFitModel.from_pretrained("vgarg/fw_identification_model_e5_large_v5_12_02_24")
# Run inference
preds = model("Why is KOF losing share in Cuernavaca Colas MS RET Original?")
Training Details
Training Set Metrics
| Training set | Min | Median | Max | 
|---|---|---|---|
| Word count | 4 | 13.7632 | 32 | 
| Label | Training Sample Count | 
|---|---|
| 0 | 10 | 
| 1 | 10 | 
| 2 | 10 | 
| 3 | 8 | 
| 4 | 10 | 
| 5 | 10 | 
| 6 | 10 | 
| 7 | 10 | 
| 8 | 10 | 
| 9 | 10 | 
| 10 | 10 | 
| 11 | 6 | 
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch | Step | Training Loss | Validation Loss | 
|---|---|---|---|
| 0.0035 | 1 | 0.3488 | - | 
| 0.1754 | 50 | 0.1594 | - | 
| 0.3509 | 100 | 0.0872 | - | 
| 0.5263 | 150 | 0.0065 | - | 
| 0.7018 | 200 | 0.0033 | - | 
| 0.8772 | 250 | 0.0018 | - | 
| 1.0526 | 300 | 0.001 | - | 
| 1.2281 | 350 | 0.0008 | - | 
| 1.4035 | 400 | 0.0008 | - | 
| 1.5789 | 450 | 0.0006 | - | 
| 1.7544 | 500 | 0.0005 | - | 
| 1.9298 | 550 | 0.0005 | - | 
| 2.1053 | 600 | 0.0005 | - | 
| 2.2807 | 650 | 0.0004 | - | 
| 2.4561 | 700 | 0.0003 | - | 
| 2.6316 | 750 | 0.0004 | - | 
| 2.8070 | 800 | 0.0004 | - | 
| 2.9825 | 850 | 0.0004 | - | 
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.0
- Tokenizers: 0.15.1
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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Base model
intfloat/multilingual-e5-large