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BCMPIIRAB_MiniLM_ALLNew

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
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BCMPIIRAB_MiniLM_ALLNew / README.md
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metadata
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: Thank you for your email. Please go ahead and issue. Please invoice in KES
  - text: >-
      Hi, We are missing some invoices, can you please provide it. 02 - 12 -
      2020 AGENT FEE 8900784339018 $21.00 02 - 19 - 2020 AGENT FEE 0017417554160
      $22.00 02 - 19 - 2020 AGENT FEE 0017417554143 $22.00 02 - 19 - 2020 AGENT
      FEE 8900783383420 $21.00
  - text: >-
      We need your assistance with the payment for the recent office supplies
      order. Let us know once it's done.
  - text: >-
      I have reported this in November and not only was the trip supposed to be
      cancelled and credited I was double billed and the billing has not been
      corrected. The total credit should be $667.20. Please confirm this will be
      done.
  - text: >-
      The invoice for the travel arrangements needs to be settled. Kindly
      provide payment confirmation.
inference: true

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

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("mann2107/BCMPIIRAB_MiniLM_ALLNew")
# Run inference
preds = model("Thank you for your email. Please go ahead and issue. Please invoice in KES")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 25.6577 136
Label Training Sample Count
0 24
1 24
2 24
3 24
4 24
5 24
6 24
7 24
8 24
9 24
10 24
11 24
12 24
13 24

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 99
  • body_learning_rate: (0.0002733656643765287, 0.0002733656643765287)
  • head_learning_rate: 2.7029049129688732e-05
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • max_length: 512
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.0002 1 0.2546 -
0.0120 50 0.1667 -
0.0241 100 0.1165 -
0.0361 150 0.0799 -
0.0481 200 0.0212 -
0.0601 250 0.0188 -
0.0722 300 0.0531 -
0.0842 350 0.0273 -
0.0962 400 0.0111 -
0.1082 450 0.0203 -
0.1203 500 0.0397 -
0.1323 550 0.0164 -
0.1443 600 0.0045 -
0.1563 650 0.0032 -
0.1684 700 0.001 -
0.1804 750 0.0011 -
0.1924 800 0.0004 -
0.2044 850 0.0009 -
0.2165 900 0.0006 -
0.2285 950 0.0008 -
0.2405 1000 0.0004 -
0.2525 1050 0.0008 -
0.2646 1100 0.0005 -
0.2766 1150 0.0006 -
0.2886 1200 0.0007 -
0.3006 1250 0.0043 -
0.3127 1300 0.0004 -
0.3247 1350 0.0005 -
0.3367 1400 0.0005 -
0.3487 1450 0.0004 -
0.3608 1500 0.0004 -
0.3728 1550 0.0005 -
0.3848 1600 0.0007 -
0.3968 1650 0.0006 -
0.4089 1700 0.0002 -
0.4209 1750 0.0006 -
0.4329 1800 0.0008 -
0.4449 1850 0.0003 -
0.4570 1900 0.0005 -
0.4690 1950 0.0003 -
0.4810 2000 0.0003 -
0.4930 2050 0.0003 -
0.5051 2100 0.0006 -
0.5171 2150 0.0003 -
0.5291 2200 0.0002 -
0.5411 2250 0.0002 -
0.5532 2300 0.0002 -
0.5652 2350 0.0004 -
0.5772 2400 0.0003 -
0.5892 2450 0.0003 -
0.6013 2500 0.0002 -
0.6133 2550 0.0002 -
0.6253 2600 0.0013 -
0.6373 2650 0.0002 -
0.6494 2700 0.0007 -
0.6614 2750 0.0004 -
0.6734 2800 0.0007 -
0.6854 2850 0.0018 -
0.6975 2900 0.0002 -
0.7095 2950 0.0003 -
0.7215 3000 0.0006 -
0.7335 3050 0.0003 -
0.7456 3100 0.0002 -
0.7576 3150 0.0002 -
0.7696 3200 0.0002 -
0.7816 3250 0.0002 -
0.7937 3300 0.0002 -
0.8057 3350 0.0001 -
0.8177 3400 0.0003 -
0.8297 3450 0.0002 -
0.8418 3500 0.0002 -
0.8538 3550 0.0002 -
0.8658 3600 0.0002 -
0.8778 3650 0.0002 -
0.8899 3700 0.0002 -
0.9019 3750 0.0005 -
0.9139 3800 0.0002 -
0.9259 3850 0.0001 -
0.9380 3900 0.0004 -
0.9500 3950 0.0001 -
0.9620 4000 0.0005 -
0.9740 4050 0.0002 -
0.9861 4100 0.0002 -
0.9981 4150 0.0001 -
1.0 4158 - 0.0302
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.1.0.dev0
  • Sentence Transformers: 3.0.1
  • Transformers: 4.42.4
  • PyTorch: 2.3.1+cu121
  • Datasets: 2.20.0
  • 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}
}