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 |
|
1 |
|
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_gpt-4o_cot-instructions_remove_final_evaluation_e1_one_big_model_17270799")
# Run inference
preds = model("Reasoning:
1. Context Grounding: The answer provided is directly supported by the document, which states, \"Allan Cox's First Class Delivery on a H128-10W for his Level 1 certification flight.\"
2. Relevance: The answer directly addresses the specific question asked, detailing the rocket and the motor used for Allan Cox's Level 1 certification flight.
3. Conciseness: The answer is clear and to the point, without any extraneous information.
Final result:")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 32 | 88.2983 | 198 |
Label | Training Sample Count |
---|---|
0 | 200 |
1 | 209 |
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 |
---|---|---|---|
0.0010 | 1 | 0.161 | - |
0.0489 | 50 | 0.2637 | - |
0.0978 | 100 | 0.2513 | - |
0.1466 | 150 | 0.151 | - |
0.1955 | 200 | 0.1002 | - |
0.2444 | 250 | 0.0596 | - |
0.2933 | 300 | 0.0383 | - |
0.3421 | 350 | 0.0236 | - |
0.3910 | 400 | 0.0121 | - |
0.4399 | 450 | 0.0075 | - |
0.4888 | 500 | 0.0046 | - |
0.5376 | 550 | 0.0031 | - |
0.5865 | 600 | 0.0029 | - |
0.6354 | 650 | 0.0031 | - |
0.6843 | 700 | 0.0017 | - |
0.7331 | 750 | 0.0016 | - |
0.7820 | 800 | 0.0014 | - |
0.8309 | 850 | 0.0013 | - |
0.8798 | 900 | 0.0014 | - |
0.9286 | 950 | 0.0015 | - |
0.9775 | 1000 | 0.0014 | - |
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_gpt-4o_cot-instructions_remove_final_evaluation_e1_one_big_model_17270799
Base model
BAAI/bge-base-en-v1.5