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Zlovoblachko
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dimension3_setfit

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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dimension3_setfit / README.md
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metadata
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
  - f1
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget: []
inference: true
model-index:
  - name: SetFit with sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: f1
            value: 0.5494505494505495
            name: F1

SetFit with sentence-transformers/all-MiniLM-L6-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-MiniLM-L6-v2 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

Evaluation

Metrics

Label F1
all 0.5495

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("Zlovoblachko/dimension3_setfit")
# Run inference
preds = model("I loved the spiderman movie!")

Training Details

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • body_learning_rate: (2.260895905036282e-05, 2.260895905036282e-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
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0004 1 0.3835 -
0.0177 50 0.3106 -
0.0353 100 0.3232 -
0.0530 150 0.319 -
0.0706 200 0.3146 -
0.0883 250 0.3194 -
0.1059 300 0.3166 -
0.1236 350 0.2941 -
0.1412 400 0.3289 -
0.1589 450 0.3108 -
0.1766 500 0.3099 -
0.1942 550 0.3072 -
0.2119 600 0.2994 -
0.2295 650 0.3062 -
0.2472 700 0.3046 -
0.2648 750 0.3086 -
0.2825 800 0.3039 -
0.3001 850 0.3096 -
0.3178 900 0.3134 -
0.3355 950 0.2965 -
0.3531 1000 0.3147 -
0.3708 1050 0.317 -
0.3884 1100 0.3123 -
0.4061 1150 0.3221 -
0.4237 1200 0.2971 -
0.4414 1250 0.2928 -
0.4590 1300 0.2977 -
0.4767 1350 0.3268 -
0.4944 1400 0.2785 -
0.5120 1450 0.3156 -
0.5297 1500 0.3148 -
0.5473 1550 0.2909 -
0.5650 1600 0.3225 -
0.5826 1650 0.3072 -
0.6003 1700 0.3099 -
0.6179 1750 0.311 -
0.6356 1800 0.3213 -
0.6532 1850 0.2937 -
0.6709 1900 0.3177 -
0.6886 1950 0.3088 -
0.7062 2000 0.3017 -
0.7239 2050 0.3076 -
0.7415 2100 0.3164 -
0.7592 2150 0.295 -
0.7768 2200 0.2957 -
0.7945 2250 0.3064 -
0.8121 2300 0.3146 -
0.8298 2350 0.3114 -
0.8475 2400 0.3151 -
0.8651 2450 0.3033 -
0.8828 2500 0.3039 -
0.9004 2550 0.3152 -
0.9181 2600 0.3185 -
0.9357 2650 0.2927 -
0.9534 2700 0.3174 -
0.9710 2750 0.3003 -
0.9887 2800 0.3157 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.1.0
  • Sentence Transformers: 3.2.1
  • Transformers: 4.44.2
  • PyTorch: 2.5.0+cu121
  • Datasets: 3.0.2
  • Tokenizers: 0.19.1

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}
}