Hugging Face's logo

Zlovoblachko
/
L1-classifier

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
Edit model card

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

Model Labels

Label Examples
Word form transmission
  • "Mother should take care of her own child at first, by this quote we simply can see that problems of government's own country should be placed on the first position."
  • "A building's style may say a lot about its history."
  • 'A lot of artists and entertainment organisations have financional costs because of free using of their contents in the Internet.'
Tense semantics
  • 'Samsung, "Blackberry" and "HTC" in 2015 have almost the same percentage share.'
  • '(5,9%) Overall, almost all unemployment rates have remained on the same level between 2014 and 2015, except EU, Latin America and Middle East.'
  • '15% consist of things which are transported by rail in Eastern Europe in 2008.'
Synonyms
  • '(the destination between Moscow and Saint Petersburg, for instance, can be easily overcame by "Lastochka" train for 5 hours).'
  • '(the destination between Moscow and Saint Petersburg, for instance, can be easily overcame by "Lastochka" train for 5 hours).'
  • 'There is an extremely clear difference: there are too many men on a tech subjects.'
Copying expression
  • '15-59 years people in Yemen are increasing, while in Italy this number decreases.'
  • '2013 year is a key one.'
  • '3,6% are people have age 60+ years.'
Transliteration
  • 'A closer look at graphic revails that goods transported by rail had good products, which massive 11%.'
  • "According to first diagramm, half of Yemen's population in 2000 was children 0-14 years old."
  • 'According to my opinion different fabrics make much more harm for our nature.'

Evaluation

Metrics

Label Accuracy
all 0.6197

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("Zlovoblachko/L1-classifier")
# Run inference
preds = model("After 1980 part old people in USA rose slight and in Sweden this point stay unchanged.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 21.005 47
Label Training Sample Count
Synonyms 99
Copying expression 26
Tense semantics 27
Word form transmission 40
Transliteration 8

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
0.0012 1 0.3375 -
0.0590 50 0.3628 -
0.1179 100 0.3312 -
0.1769 150 0.2342 -
0.2358 200 0.2665 -
0.2948 250 0.1857 -
0.3538 300 0.2134 -
0.4127 350 0.1786 -
0.4717 400 0.092 -
0.5307 450 0.2031 -
0.5896 500 0.1449 -
0.6486 550 0.1234 -
0.7075 600 0.0552 -
0.7665 650 0.0693 -
0.8255 700 0.097 -
0.8844 750 0.0448 -
0.9434 800 0.041 -
1.0024 850 0.0431 -
1.0613 900 0.0227 -
1.1203 950 0.061 -
1.1792 1000 0.0209 -
1.2382 1050 0.0071 -
1.2972 1100 0.0285 -
1.3561 1150 0.0039 -
1.4151 1200 0.0029 -
1.4741 1250 0.0097 -
1.5330 1300 0.0076 -
1.5920 1350 0.0021 -
1.6509 1400 0.015 -
1.7099 1450 0.0027 -
1.7689 1500 0.0204 -
1.8278 1550 0.013 -
1.8868 1600 0.0222 -
1.9458 1650 0.0427 -
2.0047 1700 0.0181 -
2.0637 1750 0.0232 -
2.1226 1800 0.0053 -
2.1816 1850 0.0169 -
2.2406 1900 0.006 -
2.2995 1950 0.0108 -
2.3585 2000 0.0034 -
2.4175 2050 0.0198 -
2.4764 2100 0.0006 -
2.5354 2150 0.0142 -
2.5943 2200 0.0038 -
2.6533 2250 0.0006 -
2.7123 2300 0.0007 -
2.7712 2350 0.0012 -
2.8302 2400 0.0003 -
2.8892 2450 0.0127 -
2.9481 2500 0.0181 -
3.0071 2550 0.006 -
3.0660 2600 0.0006 -
3.125 2650 0.0156 -
3.1840 2700 0.0427 -
3.2429 2750 0.0004 -
3.3019 2800 0.0013 -
3.3608 2850 0.0241 -
3.4198 2900 0.0004 -
3.4788 2950 0.0048 -
3.5377 3000 0.0004 -
3.5967 3050 0.0006 -
3.6557 3100 0.0044 -
3.7146 3150 0.0142 -
3.7736 3200 0.005 -
3.8325 3250 0.0022 -
3.8915 3300 0.0033 -
3.9505 3350 0.0033 -
4.0094 3400 0.0005 -
4.0684 3450 0.0299 -
4.1274 3500 0.0172 -
4.1863 3550 0.0079 -
4.2453 3600 0.0012 -
4.3042 3650 0.0093 -
4.3632 3700 0.0175 -
4.4222 3750 0.0278 -
4.4811 3800 0.0004 -
4.5401 3850 0.0054 -
4.5991 3900 0.002 -
4.6580 3950 0.0248 -
4.7170 4000 0.0173 -
4.7759 4050 0.0004 -
4.8349 4100 0.0154 -
4.8939 4150 0.0162 -
4.9528 4200 0.0052 -
5.0118 4250 0.0142 -
5.0708 4300 0.0109 -
5.1297 4350 0.0003 -
5.1887 4400 0.0002 -
5.2476 4450 0.0003 -
5.3066 4500 0.0081 -
5.3656 4550 0.0005 -
5.4245 4600 0.0229 -
5.4835 4650 0.0002 -
5.5425 4700 0.0004 -
5.6014 4750 0.0233 -
5.6604 4800 0.0086 -
5.7193 4850 0.0084 -
5.7783 4900 0.0177 -
5.8373 4950 0.0102 -
5.8962 5000 0.017 -
5.9552 5050 0.0037 -
6.0142 5100 0.005 -
6.0731 5150 0.0002 -
6.1321 5200 0.0188 -
6.1910 5250 0.0037 -
6.25 5300 0.0003 -
6.3090 5350 0.0137 -
6.3679 5400 0.0107 -
6.4269 5450 0.0045 -
6.4858 5500 0.0002 -
6.5448 5550 0.0238 -
6.6038 5600 0.0209 -
6.6627 5650 0.0003 -
6.7217 5700 0.0002 -
6.7807 5750 0.0029 -
6.8396 5800 0.0177 -
6.8986 5850 0.0165 -
6.9575 5900 0.0045 -
7.0165 5950 0.0203 -
7.0755 6000 0.0048 -
7.1344 6050 0.0251 -
7.1934 6100 0.0147 -
7.2524 6150 0.0033 -
7.3113 6200 0.0166 -
7.3703 6250 0.0129 -
7.4292 6300 0.0169 -
7.4882 6350 0.0001 -
7.5472 6400 0.0002 -
7.6061 6450 0.0029 -
7.6651 6500 0.0264 -
7.7241 6550 0.0079 -
7.7830 6600 0.0002 -
7.8420 6650 0.0157 -
7.9009 6700 0.0116 -
7.9599 6750 0.0031 -
8.0189 6800 0.0055 -
8.0778 6850 0.0113 -
8.1368 6900 0.0004 -
8.1958 6950 0.0301 -
8.2547 7000 0.0002 -
8.3137 7050 0.0169 -
8.3726 7100 0.0001 -
8.4316 7150 0.0165 -
8.4906 7200 0.0201 -
8.5495 7250 0.0168 -
8.6085 7300 0.0197 -
8.6675 7350 0.0165 -
8.7264 7400 0.0165 -
8.7854 7450 0.0002 -
8.8443 7500 0.0134 -
8.9033 7550 0.0037 -
8.9623 7600 0.0043 -
9.0212 7650 0.0001 -
9.0802 7700 0.0034 -
9.1392 7750 0.0036 -
9.1981 7800 0.0001 -
9.2571 7850 0.0069 -
9.3160 7900 0.0304 -
9.375 7950 0.0203 -
9.4340 8000 0.0002 -
9.4929 8050 0.0002 -
9.5519 8100 0.0058 -
9.6108 8150 0.0141 -
9.6698 8200 0.0031 -
9.7288 8250 0.0169 -
9.7877 8300 0.0002 -
9.8467 8350 0.0075 -
9.9057 8400 0.0192 -
9.9646 8450 0.0588 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.1.0.dev0
  • Sentence Transformers: 2.6.1
  • Transformers: 4.38.2
  • PyTorch: 2.2.1+cu121
  • Datasets: 2.18.0
  • Tokenizers: 0.15.2

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}
}
Downloads last month
2
Safetensors
Model size
22.7M params
Tensor type
F32
·

Finetuned from

Evaluation results

View on Papers With Code