SetFit with BAAI/bge-base-en-v1.5
This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-base-en-v1.5 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: BAAI/bge-base-en-v1.5
- 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 |
---|---|
0 |
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1 |
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Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.6901 |
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("Netta1994/setfit_baai_cybereason_gpt-4o_cot-instructions_remove_final_evaluation_e1_one_out_172")
# Run inference
preds = model("The percentage in the response status column indicates the total amount of successful completion of response actions.
Reasoning:
1. **Context Grounding**: The answer is well-supported by the document which states, \"percentage indicates the total amount of successful completion of response actions.\"
2. **Relevance**: The answer directly addresses the specific question asked about what the percentage in the response status column indicates.
3. **Conciseness**: The answer is succinct and to the point without unnecessary information.
4. **Specificity**: The answer is specific to what is being asked, detailing exactly what the percentage represents.
5. **Accuracy**: The answer provides the correct key/value as per the document.
Final result:")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 32 | 103.2508 | 245 |
Label | Training Sample Count |
---|---|
0 | 312 |
1 | 322 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0006 | 1 | 0.2802 | - |
0.0315 | 50 | 0.2628 | - |
0.0631 | 100 | 0.2537 | - |
0.0946 | 150 | 0.2553 | - |
0.1262 | 200 | 0.2546 | - |
0.1577 | 250 | 0.2477 | - |
0.1893 | 300 | 0.2189 | - |
0.2208 | 350 | 0.1745 | - |
0.2524 | 400 | 0.1426 | - |
0.2839 | 450 | 0.0785 | - |
0.3155 | 500 | 0.0569 | - |
0.3470 | 550 | 0.0402 | - |
0.3785 | 600 | 0.0325 | - |
0.4101 | 650 | 0.023 | - |
0.4416 | 700 | 0.0098 | - |
0.4732 | 750 | 0.017 | - |
0.5047 | 800 | 0.0129 | - |
0.5363 | 850 | 0.0087 | - |
0.5678 | 900 | 0.0027 | - |
0.5994 | 950 | 0.0058 | - |
0.6309 | 1000 | 0.0044 | - |
0.6625 | 1050 | 0.0046 | - |
0.6940 | 1100 | 0.0052 | - |
0.7256 | 1150 | 0.0021 | - |
0.7571 | 1200 | 0.0015 | - |
0.7886 | 1250 | 0.0013 | - |
0.8202 | 1300 | 0.0014 | - |
0.8517 | 1350 | 0.0012 | - |
0.8833 | 1400 | 0.0013 | - |
0.9148 | 1450 | 0.0012 | - |
0.9464 | 1500 | 0.0013 | - |
0.9779 | 1550 | 0.0013 | - |
Framework Versions
- Python: 3.10.14
- SetFit: 1.1.0
- Sentence Transformers: 3.1.1
- Transformers: 4.44.0
- PyTorch: 2.4.0+cu121
- Datasets: 3.0.0
- Tokenizers: 0.19.1
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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Model tree for Netta1994/setfit_baai_cybereason_gpt-4o_cot-instructions_remove_final_evaluation_e1_one_out_172
Base model
BAAI/bge-base-en-v1.5