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 SetFitHead 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
The purpose of this model is to predict multiple labels simultaneously from a given input data. Specifically, the model will predict 3 labels - GHGLabel, NetzeroLabel, NonGHGLabel- that are relevant to a particular task or application
- GHGLabel: GHG targets refer to contributions framed as targeted
outcomes in GHG terms - NetzeroLabel: Identifies if it contains Netzero Target or not.
- NonGHGLabel: Target not in terms of GHG, like energy efficiency, expansion of Solar Energy production etc.
Model Description
- Model Type: SetFit
- Sentence Transformer body: BAAI/bge-base-en-v1.5
- Classification head: a SetFitHead instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 3 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
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("GIZ/SUBTARGET_multilabel_bge")
# Run inference
preds = model("This document enfolds Iceland’s first communication on its long-term strategy (LTS), to be updated when further analysis and policy documents are published on the matter. Iceland is committed to reducing its overall greenhouse gas emissions and reaching climate neutrality no later than 2040 and become fossil fuel free in 2050, which should set Iceland on a path to net negative emissions.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 19 | 78.5467 | 173 |
Training Dataset: 728
Class Positive Count of Class GHGLabel 440 NetzeroLabel 120 NonGHGLabel 259 Validation Dataset: 80
Class Positive Count of Class GHGLabel 49 NetzeroLabel 11 NonGHGLabel 30
Training Hyperparameters
- batch_size: (8, 2)
- num_epochs: (1, 0)
- max_steps: -1
- sampling_strategy: undersampling
- body_learning_rate: (6.86e-06, 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: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Embedding Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0000 | 1 | 0.2227 | - |
0.1519 | 5000 | 0.015 | 0.0831 |
0.3038 | 10000 | 0.0146 | 0.0924 |
0.4557 | 15000 | 0.0197 | 0.0827 |
0.6076 | 20000 | 0.0031 | 0.0883 |
0.7595 | 25000 | 0.0439 | 0.0865 |
0.9114 | 30000 | 0.0029 | 0.0914 |
label | precision | recall | f1-score | support |
---|---|---|---|---|
GHG | 0.884 | 0.938 | 0.910 | 49.0 |
Netzero | 0.846 | 1.000 | 0.916 | 11.0 |
NonGHG | 0.903 | 0.933 | 0.918 | 30.0 |
Environmental Impact
Carbon emissions were measured using CodeCarbon.
- Carbon Emitted: 0.268 kg of CO2
- Hours Used: 2.03 hours
Training Hardware
- On Cloud: No
- GPU Model: 1 x Tesla V100-SXM2-16GB
- CPU Model: Intel(R) Xeon(R) CPU @ 2.20GHz
- RAM Size: 12.67 GB
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: 0.15.2
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-base-en-v1.5