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vinulacs
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sinmix-setfit-sentiment

Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
text-embeddings-inference
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sinmix-setfit-sentiment / README.md
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metadata 
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: Also you guys are deducting data so much without using it seems
  - text: ඇත්තටම ගහෝල්ඩ් වටිනවා මම 285 පැකේජය පාවිච්චි කරනවා good
  - text: Gampola Lebsack chater
  - text: >-
      Unlimited A2A calls සමඟ අඩුම ගානට වැඩිම data දෙන පට්ට pack එක
      smiling_face_with_sunglasses
  - text: >-
      he shows hw experienced n great he is.. dats da whole turning poi t of da
      match..
metrics:
  - accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: intfloat/multilingual-e5-large

SetFit with intfloat/multilingual-e5-large

This is a SetFit model that can be used for Text Classification. This SetFit model uses intfloat/multilingual-e5-large 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
0
  • 'Fiber nae.'
  • 'hey Zemlak boru bill danne ai ewa refund karana kiwata reply na anthima horu tikakne'
  • 'hey Zemlak boru bill danne ai ewa refund karana kiwata reply na horu tikakne'
2
  • 'I think this is the first time Pubg players are awarded with this much of a cash pool in SL.'
  • 'I think this is the first time players are with this of a pool in'
  • 'I think this is the first time Pubg players are awarded with in this much of a cash pool in SL.'
1
  • 'Koo ampara district alamkulam area 2g coverage'
  • 'Koo ampara district area 2g coverage'
  • 'Koo ampara district district alamkulam area 2g coverage'

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("vinulacs/sinmix-setfit-sentiment")
# Run inference
preds = model("Gampola Lebsack chater")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 9.7390 79
Label Training Sample Count
0 194
1 185
2 165

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 40
  • 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
  • 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 0.2675 -
0.0184 50 0.3054 -
0.0368 100 0.219 -
0.0551 150 0.1664 -
0.0735 200 0.0785 -
0.0919 250 0.0256 -
0.1103 300 0.009 -
0.1287 350 0.0104 -
0.1471 400 0.0004 -
0.1654 450 0.0003 -
0.1838 500 0.0003 -
0.2022 550 0.0002 -
0.2206 600 0.0002 -
0.2390 650 0.0002 -
0.2574 700 0.0002 -
0.2757 750 0.0002 -
0.2941 800 0.0001 -
0.3125 850 0.0001 -
0.3309 900 0.0001 -
0.3493 950 0.0001 -
0.3676 1000 0.0001 -
0.3860 1050 0.0001 -
0.4044 1100 0.0001 -
0.4228 1150 0.0001 -
0.4412 1200 0.0001 -
0.4596 1250 0.0001 -
0.4779 1300 0.0001 -
0.4963 1350 0.0001 -
0.5147 1400 0.0001 -
0.5331 1450 0.0001 -
0.5515 1500 0.0001 -
0.5699 1550 0.0001 -
0.5882 1600 0.0001 -
0.6066 1650 0.0001 -
0.625 1700 0.0001 -
0.6434 1750 0.0001 -
0.6618 1800 0.0001 -
0.6801 1850 0.0001 -
0.6985 1900 0.0001 -
0.7169 1950 0.0001 -
0.7353 2000 0.0001 -
0.7537 2050 0.0001 -
0.7721 2100 0.0001 -
0.7904 2150 0.0001 -
0.8088 2200 0.0001 -
0.8272 2250 0.0001 -
0.8456 2300 0.0001 -
0.8640 2350 0.0001 -
0.8824 2400 0.0001 -
0.9007 2450 0.0001 -
0.9191 2500 0.0001 -
0.9375 2550 0.0013 -
0.9559 2600 0.0001 -
0.9743 2650 0.0001 -
0.9926 2700 0.0001 -

Framework Versions

  • Python: 3.12.12
  • SetFit: 1.1.3
  • Sentence Transformers: 5.2.3
  • Transformers: 4.51.3
  • PyTorch: 2.10.0+cu128
  • Datasets: 4.0.0
  • Tokenizers: 0.21.4

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}
}