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Jorgeutd
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setfit-bge-small-v1.5-sst2-50-shot

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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setfit-bge-small-v1.5-sst2-50-shot / README.md
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: BAAI/bge-small-en-v1.5
metrics:
  - accuracy
widget:
  - text: >-
      mostly works because of the universal themes , earnest performances ...
      and excellent use of music by india 's popular gulzar and jagjit singh .
  - text: >-
      in all the annals of the movies , few films have been this odd ,
      inexplicable and unpleasant .
  - text: >-
      director charles stone iii applies more detail to the film 's music than
      to the story line ; what 's best about drumline is its energy .
  - text: >-
      there 's nothing exactly wrong here , but there 's not nearly enough that
      's right .
  - text: it 's a bad sign in a thriller when you instantly know whodunit .
pipeline_tag: text-classification
inference: true
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: 0.8621636463481603
            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
1
  • 'a sensitive , modest comic tragedy that works as both character study and symbolic examination of the huge economic changes sweeping modern china .'
  • 'the year 2002 has conjured up more coming-of-age stories than seem possible , but take care of my cat emerges as the very best of them .'
  • 'amy and matthew have a bit of a phony relationship , but the film works in spite of it .'
0
  • 'works on the whodunit level as its larger themes get lost in the murk of its own making'
  • "one of those strained caper movies that 's hardly any fun to watch and begins to vaporize from your memory minutes after it ends ."
  • "shunji iwai 's all about lily chou chou is a beautifully shot , but ultimately flawed film about growing up in japan ."

Evaluation

Metrics

Label Accuracy
all 0.8622

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("Jorgeutd/setfit-bge-small-v1.5-sst2-50-shot")
# Run inference
preds = model("it 's a bad sign in a thriller when you instantly know whodunit .")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 3 21.31 50
Label Training Sample Count
0 50
1 50

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (10, 10)
  • 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
0.0031 1 0.2515 -
0.1567 50 0.2298 -
0.3135 100 0.2134 -
0.4702 150 0.0153 -
0.6270 200 0.0048 -
0.7837 250 0.0024 -
0.9404 300 0.0023 -
1.0972 350 0.0016 -
1.2539 400 0.0016 -
1.4107 450 0.001 -
1.5674 500 0.0013 -
1.7241 550 0.0008 -
1.8809 600 0.0008 -
2.0376 650 0.0007 -
2.1944 700 0.0008 -
2.3511 750 0.0008 -
2.5078 800 0.0007 -
2.6646 850 0.0006 -
2.8213 900 0.0006 -
2.9781 950 0.0005 -
3.1348 1000 0.0006 -
3.2915 1050 0.0006 -
3.4483 1100 0.0005 -
3.6050 1150 0.0005 -
3.7618 1200 0.0005 -
3.9185 1250 0.0005 -
4.0752 1300 0.0005 -
4.2320 1350 0.0004 -
4.3887 1400 0.0004 -
4.5455 1450 0.0004 -
4.7022 1500 0.0003 -
4.8589 1550 0.0006 -
5.0157 1600 0.0007 -
5.1724 1650 0.0004 -
5.3292 1700 0.0004 -
5.4859 1750 0.0004 -
5.6426 1800 0.0004 -
5.7994 1850 0.0003 -
5.9561 1900 0.0004 -
6.1129 1950 0.0003 -
6.2696 2000 0.0003 -
6.4263 2050 0.0005 -
6.5831 2100 0.0003 -
6.7398 2150 0.0003 -
6.8966 2200 0.0003 -
7.0533 2250 0.0003 -
7.2100 2300 0.0003 -
7.3668 2350 0.0003 -
7.5235 2400 0.0002 -
7.6803 2450 0.0003 -
7.8370 2500 0.0003 -
7.9937 2550 0.0003 -
8.1505 2600 0.0003 -
8.3072 2650 0.0003 -
8.4639 2700 0.0003 -
8.6207 2750 0.0003 -
8.7774 2800 0.0004 -
8.9342 2850 0.0002 -
9.0909 2900 0.0003 -
9.2476 2950 0.0004 -
9.4044 3000 0.0004 -
9.5611 3050 0.0003 -
9.7179 3100 0.0004 -
9.8746 3150 0.0003 -

Framework Versions

  • Python: 3.10.13
  • SetFit: 1.0.3
  • Sentence Transformers: 2.6.1
  • Transformers: 4.39.1
  • PyTorch: 2.1.0
  • 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}
}