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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: Ruukki Group calculates that it has lost EUR 4mn in the failed project .
  - text: >-
      The Tecnomen Convergent Charging solution includes functionality for
      prepaid and post-paid billing , charging and rating of voice calls , video
      calls , raw data traffic and any type of content services in both mobile
      and fixed networks .
  - text: The combined value of the planned investments is about EUR 30mn .
  - text: >-
      The Diameter Protocol is developed according to the standards IETF RFC
      3588 and IETF RFC 3539 .
  - text: >-
      Below are unaudited consolidated results for Aspocomp Group under IFRS
      reporting standards .
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-small-en-v1.5
model-index:
  - name: SetFit with BAAI/bge-small-en-v1.5
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.9426048565121413
            name: Accuracy

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:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
positive
  • 'HELSINKI ( AFX ) - Nokian Tyres reported a fourth quarter pretax profit of 61.5 mln eur , up from 48.6 mln on the back of strong sales .'
  • 'Equity ratio was 60.9 % compared to 54.2 % In the third quarter of 2007 , net sales of the Frozen Foods Business totaled EUR 11.0 , up by about 5 % from the third quarter of 2006 .'
  • "`` After a long , unprofitable period the Food Division posted a profitable result , which speaks of a healthier cost structure and a new approach in business operations , '' Rihko said ."
neutral
  • 'Their names have not yet been released .'
  • 'The contract includes design , construction , delivery of equipment , installation and commissioning .'
  • "Tieto 's service is also used to send , process and receive materials related to absentee voting ."
negative
  • 'The company confirmed its estimate for lower revenue for the whole 2009 than the year-ago EUR93 .9 m as given in the interim report on 5 August 2009 .'
  • 'Acando AB ( ACANB SS ) fell 8.9 percent to 13.35 kronor , the lowest close since Dec. 11 .'
  • 'Okmetic expects its net sales for the first half of 2009 to be less than in 2008 .'

Evaluation

Metrics

Label Accuracy
all 0.9426

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("moshew/bge-small-en-v1.5-SetFit-FSA")
# Run inference
preds = model("The combined value of the planned investments is about EUR 30mn .")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 22.4020 60
Label Training Sample Count
negative 266
neutral 1142
positive 403

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 10
  • 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
0.0004 1 0.2832 -
0.0221 50 0.209 -
0.0442 100 0.1899 -
0.0663 150 0.1399 -
0.0883 200 0.1274 -
0.1104 250 0.0586 -
0.1325 300 0.0756 -
0.1546 350 0.0777 -
0.1767 400 0.0684 -
0.1988 450 0.0311 -
0.2208 500 0.0102 -
0.2429 550 0.052 -
0.2650 600 0.0149 -
0.2871 650 0.1042 -
0.3092 700 0.061 -
0.3313 750 0.0083 -
0.3534 800 0.0036 -
0.3754 850 0.002 -
0.3975 900 0.0598 -
0.4196 950 0.0036 -
0.4417 1000 0.0027 -
0.4638 1050 0.0617 -
0.4859 1100 0.0015 -
0.5080 1150 0.0022 -
0.5300 1200 0.0016 -
0.5521 1250 0.0009 -
0.5742 1300 0.0013 -
0.5963 1350 0.0009 -
0.6184 1400 0.0015 -
0.6405 1450 0.0018 -
0.6625 1500 0.0015 -
0.6846 1550 0.0018 -
0.7067 1600 0.0016 -
0.7288 1650 0.0022 -
0.7509 1700 0.0013 -
0.7730 1750 0.0108 -
0.7951 1800 0.0016 -
0.8171 1850 0.0021 -
0.8392 1900 0.002 -
0.8613 1950 0.0015 -
0.8834 2000 0.0016 -
0.9055 2050 0.0028 -
0.9276 2100 0.0013 -
0.9496 2150 0.0019 -
0.9717 2200 0.0075 -
0.9938 2250 0.0015 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.5.1
  • Transformers: 4.38.1
  • PyTorch: 2.1.0+cu121
  • Datasets: 2.18.0
  • 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}
}