SetFit with BAAI/bge-small-en-v1.5
This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-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-small-en-v1.5
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 4 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 |
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Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 1.0 |
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("Gopal2002/NASFUND_MODEL")
# Run inference
preds = model("b'nasfund \\& National Superannuation fund Ltd\n\n \n\nP.O. Box 5791 Boroko PNG\nTelephone: (675) 313 1813 PURCHASE ORDER\nEmail:\nSupplier Details:- Order No: PF006716\nProperty PNG Requested by: Gareth Kobua\nP.O.Box 1067 Contact No:\nPapua New Guinea\nDate Issued: 25-Jul-2023\nSupplier No: 00469\nDelivery Date: 25-Jul-2023\nPage: 1 of 1\nAttention :\nDeliver To: Invoice To:\nNational Superannuation fund Ltd\nBSP Haus Poreporena Freeway\nLevel 4\nDescription Qty. Unit at ee i\n1 Service Fee for the External Property 0 ONLY 0.00 30,000.00\nValuation Service for Credit Corp. Property Portfolio.\nOrder Total PGK : 30,000.00\n\nApproved By: Nathan KWARARA 25-Jul-2023\nRequisitioned By: Niasul KISOKAU 25-Jul-2023\n\nSignature\n\x0c'")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 68 | 191.1579 | 417 |
| Label | Training Sample Count |
|---|---|
| 0 | 4 |
| 1 | 4 |
| 2 | 5 |
| 3 | 6 |
Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.1111 | 1 | 0.3014 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.0
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- 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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Base model
BAAI/bge-small-en-v1.5