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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:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. 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

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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