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nksk
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Intent_bge-small-en-v1.5_v5.0

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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Intent_bge-small-en-v1.5_v5.0 / README.md
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metadata
base_model: BAAI/bge-small-en-v1.5
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: I've exhausted all my knowledge on this question
  - text: That's all I can offer for this question at this time
  - text: >-
      I believe user engagement and time spent on the platform for Spotify's
      success are crucial. I also believe that it's crucial to focus on
      providing personalized recommendations and a seamless user experience to
      keep users engaged. Anything else that you would like me to consider or
      key points that I may have missed?
  - text: >-
      so, here's the gist of my recommendation: we need to focus on three areas
      - execution, marketing, and sales. with that I have captured my key
      approach here. anything else you want me to address?
  - text: Let me revisit something you mentioned earlier.
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: 0.9054054054054054
            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
none
  • 'I’ll need to think it over to elaborate on this question.'
  • 'I think I will go to Disneyland.'
  • 'I missed part of that; could you please rephrase it for me?'
wrapup_question
  • "That's all for now in regards to this question"
  • "Do you have any other issues you'd like me to address?"
  • 'Do you have any other questions related to this topic?'
end_question
  • "let's do some other more meaningful question"
  • "I think I've covered everything I needed to for this question"
  • 'Ok, I am done answering this question'
next_question
  • 'Can you please provide me a different question?'
  • "I've given that question a lot of thought. What's next?"
  • "I hope I answered your question to your satisfaction. What's the next one?"

Evaluation

Metrics

Label Accuracy
all 0.9054

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("nksk/Intent_bge-small-en-v1.5_v5.0")
# Run inference
preds = model("Let me revisit something you mentioned earlier.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 38.7075 1048
Label Training Sample Count
end_question 56
next_question 30
none 157
wrapup_question 51

Training Hyperparameters

  • batch_size: (32, 16)
  • num_epochs: (3, 10)
  • max_steps: -1
  • sampling_strategy: oversampling
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.0005
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: True
  • use_amp: True
  • 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
0.0006 1 0.2718 -
0.0290 50 0.2554 -
0.0580 100 0.2373 -
0.0870 150 0.2127 -
0.1160 200 0.1728 -
0.1450 250 0.1301 -
0.1740 300 0.0944 -
0.2030 350 0.0591 -
0.2320 400 0.0393 -
0.2610 450 0.0217 -
0.2900 500 0.013 -
0.3190 550 0.0111 -
0.3480 600 0.006 -
0.3770 650 0.0047 -
0.4060 700 0.0035 -
0.4350 750 0.004 -
0.4640 800 0.0022 -
0.4930 850 0.0019 -
0.5220 900 0.0017 -
0.5510 950 0.0014 -
0.5800 1000 0.0013 -
0.6090 1050 0.0013 -
0.6381 1100 0.0012 -
0.6671 1150 0.0011 -
0.6961 1200 0.001 -
0.7251 1250 0.0009 -
0.7541 1300 0.0009 -
0.7831 1350 0.0009 -
0.8121 1400 0.0008 -
0.8411 1450 0.0008 -
0.8701 1500 0.0008 -
0.8991 1550 0.0007 -
0.9281 1600 0.0008 -
0.9571 1650 0.0007 -
0.9861 1700 0.0007 -
1.0151 1750 0.0007 -
1.0441 1800 0.0006 -
1.0731 1850 0.0006 -
1.1021 1900 0.0006 -
1.1311 1950 0.0006 -
1.1601 2000 0.0006 -
1.1891 2050 0.0006 -
1.2181 2100 0.0006 -
1.2471 2150 0.0006 -
1.2761 2200 0.0005 -
1.3051 2250 0.0005 -
1.3341 2300 0.0005 -
1.3631 2350 0.0005 -
1.3921 2400 0.0005 -
1.4211 2450 0.0005 -
1.4501 2500 0.0005 -
1.4791 2550 0.0005 -
1.5081 2600 0.0005 -
1.5371 2650 0.0004 -
1.5661 2700 0.0005 -
1.5951 2750 0.0005 -
1.6241 2800 0.0004 -
1.6531 2850 0.0004 -
1.6821 2900 0.0004 -
1.7111 2950 0.0004 -
1.7401 3000 0.0004 -
1.7691 3050 0.0004 -
1.7981 3100 0.0004 -
1.8271 3150 0.0004 -
1.8561 3200 0.0004 -
1.8852 3250 0.0004 -
1.9142 3300 0.0004 -
1.9432 3350 0.0004 -
1.9722 3400 0.0004 -
2.0012 3450 0.0004 -
2.0302 3500 0.0003 -
2.0592 3550 0.0004 -
2.0882 3600 0.0004 -
2.1172 3650 0.0004 -
2.1462 3700 0.0004 -
2.1752 3750 0.0004 -
2.2042 3800 0.0004 -
2.2332 3850 0.0003 -
2.2622 3900 0.0003 -
2.2912 3950 0.0003 -
2.3202 4000 0.0003 -
2.3492 4050 0.0003 -
2.3782 4100 0.0003 -
2.4072 4150 0.0003 -
2.4362 4200 0.0003 -
2.4652 4250 0.0003 -
2.4942 4300 0.0003 -
2.5232 4350 0.0003 -
2.5522 4400 0.0003 -
2.5812 4450 0.0003 -
2.6102 4500 0.0003 -
2.6392 4550 0.0003 -
2.6682 4600 0.0003 -
2.6972 4650 0.0003 -
2.7262 4700 0.0003 -
2.7552 4750 0.0003 -
2.7842 4800 0.0003 -
2.8132 4850 0.0003 -
2.8422 4900 0.0003 -
2.8712 4950 0.0003 -
2.9002 5000 0.0003 -
2.9292 5050 0.0003 -
2.9582 5100 0.0003 -
2.9872 5150 0.0003 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.1.0
  • Sentence Transformers: 3.0.1
  • 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}
}