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anismahmahi
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G2-with-noPropaganda-multilabel-setfit-model

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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G2-with-noPropaganda-multilabel-setfit-model / README.md
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: But the author is Bharath Ganesh.
  - text: >-
      The documents, which suggest all the adults were involved in the training,
      say a person serving as a foster parent caring for one of the kids
      revealed the details about the training.
  - text: >-
      Louis Farrakhan, the 84-year-old head of the Nation of Islam, has been
      back in the headlines after a previously unreleased photo of him with
      President Barack Obama was published in January and Mr. Farrakhan gave an
      anti-Semitic speech at his organization’s annual convention last month.
  - text: >-
      The name of that CIA official whose torture activities the Post described
      is Gina Haspel.
  - text: This is not just about Facebook or Twitter.
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
  - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.7125193199381762
            name: Accuracy

SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A OneVsRestClassifier 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

Evaluation

Metrics

Label Accuracy
all 0.7125

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("anismahmahi/G2-with-noPropaganda-multilabel-setfit-model")
# Run inference
preds = model("But the author is Bharath Ganesh.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 23.3972 129

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (2, 2)
  • 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: True

Training Results

Epoch Step Training Loss Validation Loss
0.0003 1 0.3874 -
0.0135 50 0.3734 -
0.0270 100 0.2741 -
0.0405 150 0.2802 -
0.0539 200 0.2355 -
0.0674 250 0.2616 -
0.0809 300 0.262 -
0.0944 350 0.2302 -
0.1079 400 0.1962 -
0.1214 450 0.1438 -
0.1348 500 0.2001 -
0.1483 550 0.2126 -
0.1618 600 0.1244 -
0.1753 650 0.1968 -
0.1888 700 0.1473 -
0.2023 750 0.2407 -
0.2157 800 0.1607 -
0.2292 850 0.1376 -
0.2427 900 0.145 -
0.2562 950 0.1439 -
0.2697 1000 0.0418 -
0.2832 1050 0.0822 -
0.2967 1100 0.1042 -
0.3101 1150 0.0381 -
0.3236 1200 0.17 -
0.3371 1250 0.0253 -
0.3506 1300 0.1009 -
0.3641 1350 0.1355 -
0.3776 1400 0.0314 -
0.3910 1450 0.2185 -
0.4045 1500 0.0774 -
0.4180 1550 0.0512 -
0.4315 1600 0.0814 -
0.4450 1650 0.0169 -
0.4585 1700 0.0591 -
0.4720 1750 0.1232 -
0.4854 1800 0.0941 -
0.4989 1850 0.1024 -
0.5124 1900 0.0031 -
0.5259 1950 0.037 -
0.5394 2000 0.1418 -
0.5529 2050 0.0685 -
0.5663 2100 0.0326 -
0.5798 2150 0.0143 -
0.5933 2200 0.064 -
0.6068 2250 0.0612 -
0.6203 2300 0.0689 -
0.6338 2350 0.1402 -
0.6472 2400 0.288 -
0.6607 2450 0.0075 -
0.6742 2500 0.0785 -
0.6877 2550 0.0339 -
0.7012 2600 0.0668 -
0.7147 2650 0.0319 -
0.7282 2700 0.0622 -
0.7416 2750 0.1169 -
0.7551 2800 0.0249 -
0.7686 2850 0.0218 -
0.7821 2900 0.0621 -
0.7956 2950 0.0698 -
0.8091 3000 0.0562 -
0.8225 3050 0.0412 -
0.8360 3100 0.0048 -
0.8495 3150 0.0085 -
0.8630 3200 0.0122 -
0.8765 3250 0.0387 -
0.8900 3300 0.0053 -
0.9035 3350 0.0032 -
0.9169 3400 0.0156 -
0.9304 3450 0.0013 -
0.9439 3500 0.001 -
0.9574 3550 0.0009 -
0.9709 3600 0.0025 -
0.9844 3650 0.0006 -
0.9978 3700 0.0832 -
1.0 3708 - 0.2776
1.0113 3750 0.0735 -
1.0248 3800 0.0053 -
1.0383 3850 0.0614 -
1.0518 3900 0.0005 -
1.0653 3950 0.0046 -
1.0787 4000 0.0024 -
1.0922 4050 0.0004 -
1.1057 4100 0.0016 -
1.1192 4150 0.0789 -
1.1327 4200 0.0016 -
1.1462 4250 0.0018 -
1.1597 4300 0.0005 -
1.1731 4350 0.0051 -
1.1866 4400 0.0139 -
1.2001 4450 0.0021 -
1.2136 4500 0.0064 -
1.2271 4550 0.0025 -
1.2406 4600 0.0054 -
1.2540 4650 0.0022 -
1.2675 4700 0.0734 -
1.2810 4750 0.026 -
1.2945 4800 0.0004 -
1.3080 4850 0.0574 -
1.3215 4900 0.0043 -
1.3350 4950 0.0975 -
1.3484 5000 0.0125 -
1.3619 5050 0.0045 -
1.3754 5100 0.0011 -
1.3889 5150 0.0061 -
1.4024 5200 0.0004 -
1.4159 5250 0.0278 -
1.4293 5300 0.005 -
1.4428 5350 0.0302 -
1.4563 5400 0.0341 -
1.4698 5450 0.0007 -
1.4833 5500 0.0128 -
1.4968 5550 0.0459 -
1.5102 5600 0.0128 -
1.5237 5650 0.0003 -
1.5372 5700 0.004 -
1.5507 5750 0.0005 -
1.5642 5800 0.0005 -
1.5777 5850 0.001 -
1.5912 5900 0.0069 -
1.6046 5950 0.0124 -
1.6181 6000 0.0026 -
1.6316 6050 0.0143 -
1.6451 6100 0.0005 -
1.6586 6150 0.0362 -
1.6721 6200 0.0002 -
1.6855 6250 0.0608 -
1.6990 6300 0.0006 -
1.7125 6350 0.0003 -
1.7260 6400 0.0041 -
1.7395 6450 0.0045 -
1.7530 6500 0.0005 -
1.7665 6550 0.0014 -
1.7799 6600 0.0004 -
1.7934 6650 0.0211 -
1.8069 6700 0.0002 -
1.8204 6750 0.0048 -
1.8339 6800 0.0368 -
1.8474 6850 0.0107 -
1.8608 6900 0.0045 -
1.8743 6950 0.0062 -
1.8878 7000 0.0003 -
1.9013 7050 0.0001 -
1.9148 7100 0.0096 -
1.9283 7150 0.0008 -
1.9417 7200 0.0184 -
1.9552 7250 0.0006 -
1.9687 7300 0.0291 -
1.9822 7350 0.0335 -
1.9957 7400 0.0149 -
2.0 7416 - 0.2666
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.1
  • Sentence Transformers: 2.2.2
  • Transformers: 4.35.2
  • PyTorch: 2.1.0+cu121
  • Datasets: 2.16.1
  • Tokenizers: 0.15.0

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}
}