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go-emotions-multilabel

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
datasets:
  - go_emotions
metrics:
  - accuracy
widget:
  - text: Curious as to why he's been passed up so many times now.
  - text: I think you mean the announcement
  - text: >-
      try to attract the guy that i like. other than that i love gaming drawing
      writing and watching tv.
  - text: >-
      I thought that phrase was only used for memes now lol at least that's what
      I got from Vic deals
  - text: 'Fantastic read, thanks for the insights! '
pipeline_tag: text-classification
inference: false

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

This is a SetFit model trained on the go_emotions dataset 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

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("bhaskars113/go-emotions-multilabel")
# Run inference
preds = model("I think you mean the announcement")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 13.6060 30

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 20
  • body_learning_rate: (2e-05, 2e-05)
  • head_learning_rate: 2e-05
  • 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 0.3873 -
0.0223 50 0.2243 -
0.0446 100 0.2305 -
0.0670 150 0.2297 -
0.0893 200 0.2758 -
0.1116 250 0.2197 -
0.1339 300 0.1984 -
0.1562 350 0.1729 -
0.1786 400 0.1244 -
0.2009 450 0.164 -
0.2232 500 0.1587 -
0.2455 550 0.2272 -
0.2679 600 0.3367 -
0.2902 650 0.1715 -
0.3125 700 0.2213 -
0.3348 750 0.2394 -
0.3571 800 0.1275 -
0.3795 850 0.1919 -
0.4018 900 0.143 -
0.4241 950 0.2431 -
0.4464 1000 0.1747 -
0.4688 1050 0.1567 -
0.4911 1100 0.194 -
0.5134 1150 0.1895 -
0.5357 1200 0.1601 -
0.5580 1250 0.1042 -
0.5804 1300 0.0553 -
0.6027 1350 0.1614 -
0.625 1400 0.1854 -
0.6473 1450 0.1259 -
0.6696 1500 0.138 -
0.6920 1550 0.2181 -
0.7143 1600 0.1144 -
0.7366 1650 0.1987 -
0.7589 1700 0.0859 -
0.7812 1750 0.1665 -
0.8036 1800 0.1628 -
0.8259 1850 0.2296 -
0.8482 1900 0.1892 -
0.8705 1950 0.2033 -
0.8929 2000 0.1507 -
0.9152 2050 0.1592 -
0.9375 2100 0.1077 -
0.9598 2150 0.1415 -
0.9821 2200 0.1561 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.7.0
  • Transformers: 4.40.0
  • PyTorch: 2.2.1+cu121
  • Datasets: 2.19.0
  • 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}
}