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selina09
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yt_setfit

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
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yt_setfit / 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 have owned this NAS for almost a year now and actually purchased a
      second one It works flawlessly and QNAP live tech support is superb There
      is also a fairly comprehensive forum for users as well I have slowly
      upgraded my capacities as newer larger capacity drives have come out on
      the market All have been recognized and the space expanded without a hitch
      I highly recommend this product 
  - text: Good as expected
  - text: >-
      This is a very good video editing package In the past I ve only used Corel
      video editing products but Cyberlink s offering is on par It offers
      similar options but they are different enough for me to want to use both
      products depending on what I m trying to achieve There are quick uploading
      options that make it very easy to get video onto Youtube and other online
      video sites 
  - text: Works great
  - text: >-
      This is my favorite crack open the computer and amuse myself for a few
      hours software Easy to pick up if you have no prior experience with
      computer animation but advanced enough that someone with the right skills
      could pull together an impressive movie 
inference: true

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
0
  • 'Have used Turbo Tax for years Never a problem I m pretty concerned now with the news that many of their users had their returns hacked by people who gained access to Turbo Tax and stole the information Not sure I will use it next year until I research how serious this is was '
  • 'Can t beat an Apple computer Like P KB best by test '
  • 'Works for Mac or Pc but not on widows '
1
  • 'Would not install activation code not accepted Returned it '
  • 'Worth all four of the software programs which are included in this product '
  • 'The marketing information makes this software look like it should be fabulous lots of useful features that I would love to experiment with However the software just doesn t work I will keep using my very old JASC version of this software instead '

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("selina09/yt_setfit")
# Run inference
preds = model("Works great")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 34.9207 102
Label Training Sample Count
0 123
1 41

Training Hyperparameters

  • batch_size: (32, 32)
  • num_epochs: (10, 10)
  • max_steps: -1
  • sampling_strategy: oversampling
  • 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
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0019 1 0.2503 -
0.0942 50 0.2406 -
0.1883 100 0.2029 -
0.2825 150 0.2207 -
0.3766 200 0.1612 -
0.4708 250 0.0725 -
0.5650 300 0.0163 -
0.6591 350 0.0108 -
0.7533 400 0.0153 -
0.8475 450 0.0486 -
0.9416 500 0.0191 -
1.0358 550 0.0207 -
1.1299 600 0.0148 -
1.2241 650 0.0031 -
1.3183 700 0.001 -
1.4124 750 0.0287 -
1.5066 800 0.0146 -
1.6008 850 0.0147 -
1.6949 900 0.0165 -
1.7891 950 0.0008 -
1.8832 1000 0.0165 -
1.9774 1050 0.0007 -
2.0716 1100 0.0129 -
2.1657 1150 0.0143 -
2.2599 1200 0.0006 -
2.3540 1250 0.0008 -
2.4482 1300 0.0047 -
2.5424 1350 0.0005 -
2.6365 1400 0.0116 -
2.7307 1450 0.0093 -
2.8249 1500 0.0211 -
2.9190 1550 0.0076 -
3.0132 1600 0.0047 -
3.1073 1650 0.0005 -
3.2015 1700 0.0064 -
3.2957 1750 0.014 -
3.3898 1800 0.0479 -
3.4840 1850 0.0005 -
3.5782 1900 0.0045 -
3.6723 1950 0.0188 -
3.7665 2000 0.0004 -
3.8606 2050 0.0122 -
3.9548 2100 0.0004 -
4.0490 2150 0.008 -
4.1431 2200 0.0245 -
4.2373 2250 0.005 -
4.3315 2300 0.0244 -
4.4256 2350 0.0208 -
4.5198 2400 0.0237 -
4.6139 2450 0.0005 -
4.7081 2500 0.0004 -
4.8023 2550 0.02 -
4.8964 2600 0.0004 -
4.9906 2650 0.0067 -
5.0847 2700 0.0099 -
5.1789 2750 0.0138 -
5.2731 2800 0.0192 -
5.3672 2850 0.0217 -
5.4614 2900 0.0056 -
5.5556 2950 0.0003 -
5.6497 3000 0.0052 -
5.7439 3050 0.0123 -
5.8380 3100 0.0136 -
5.9322 3150 0.0221 -
6.0264 3200 0.0235 -
6.1205 3250 0.0144 -
6.2147 3300 0.0174 -
6.3089 3350 0.007 -
6.4030 3400 0.0044 -
6.4972 3450 0.0003 -
6.5913 3500 0.007 -
6.6855 3550 0.0004 -
6.7797 3600 0.0384 -
6.8738 3650 0.0055 -
6.9680 3700 0.0056 -
7.0621 3750 0.0118 -
7.1563 3800 0.0143 -
7.2505 3850 0.0289 -
7.3446 3900 0.0301 -
7.4388 3950 0.0119 -
7.5330 4000 0.012 -
7.6271 4050 0.0138 -
7.7213 4100 0.0148 -
7.8154 4150 0.0003 -
7.9096 4200 0.0268 -
8.0038 4250 0.0131 -
8.0979 4300 0.0237 -
8.1921 4350 0.0004 -
8.2863 4400 0.0211 -
8.3804 4450 0.0092 -
8.4746 4500 0.005 -
8.5687 4550 0.0056 -
8.6629 4600 0.0168 -
8.7571 4650 0.0045 -
8.8512 4700 0.0184 -
8.9454 4750 0.0049 -
9.0395 4800 0.0047 -
9.1337 4850 0.0099 -
9.2279 4900 0.0054 -
9.3220 4950 0.0185 -
9.4162 5000 0.005 -
9.5104 5050 0.0004 -
9.6045 5100 0.013 -
9.6987 5150 0.0002 -
9.7928 5200 0.0187 -
9.8870 5250 0.0003 -
9.9812 5300 0.0081 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 3.0.1
  • Transformers: 4.40.2
  • PyTorch: 2.4.0+cu121
  • Datasets: 2.21.0
  • 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}
}