Hugging Face's logo

luis-cardoso-q
/
kotodama-multilingual-v3

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

SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-mpnet-base-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

Evaluation

Metrics

Label Accuracy
all 0.7357

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("luis-cardoso-q/kotodama-multilingual-v3")
# Run inference
preds = model("2023-F48")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 2.6689 16
Label Training Sample Count
buying 25
company name 73
invoice 128
random characters 128
refund 87
rent 38
salary 128

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • 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: True

Training Results

Epoch Step Training Loss Validation Loss
0.0001 1 0.2604 -
0.0026 50 0.3244 -
0.0053 100 0.2233 -
0.0079 150 0.2034 -
0.0105 200 0.2998 -
0.0131 250 0.2074 -
0.0158 300 0.1682 -
0.0184 350 0.1815 -
0.0210 400 0.155 -
0.0237 450 0.16 -
0.0263 500 0.117 -
0.0289 550 0.1685 -
0.0315 600 0.0348 -
0.0342 650 0.0912 -
0.0368 700 0.0217 -
0.0394 750 0.0417 -
0.0421 800 0.0592 -
0.0447 850 0.047 -
0.0473 900 0.0914 -
0.0499 950 0.0116 -
0.0526 1000 0.022 -
0.0552 1050 0.0018 -
0.0578 1100 0.0159 -
0.0605 1150 0.0097 -
0.0631 1200 0.066 -
0.0657 1250 0.0027 -
0.0683 1300 0.003 -
0.0710 1350 0.0146 -
0.0736 1400 0.009 -
0.0762 1450 0.0016 -
0.0789 1500 0.001 -
0.0815 1550 0.019 -
0.0841 1600 0.0015 -
0.0867 1650 0.0003 -
0.0894 1700 0.0929 -
0.0920 1750 0.013 -
0.0946 1800 0.0007 -
0.0973 1850 0.0413 -
0.0999 1900 0.0922 -
0.1025 1950 0.0009 -
0.1051 2000 0.001 -
0.1078 2050 0.0007 -
0.1104 2100 0.0086 -
0.1130 2150 0.0017 -
0.1157 2200 0.0048 -
0.1183 2250 0.0002 -
0.1209 2300 0.0518 -
0.1235 2350 0.0271 -
0.1262 2400 0.0138 -
0.1288 2450 0.0136 -
0.1314 2500 0.0444 -
0.1341 2550 0.0096 -
0.1367 2600 0.0064 -
0.1393 2650 0.0092 -
0.1419 2700 0.0012 -
0.1446 2750 0.0044 -
0.1472 2800 0.0121 -
0.1498 2850 0.0004 -
0.1525 2900 0.0002 -
0.1551 2950 0.0008 -
0.1577 3000 0.0034 -
0.1603 3050 0.0002 -
0.1630 3100 0.0152 -
0.1656 3150 0.0195 -
0.1682 3200 0.0005 -
0.1709 3250 0.0002 -
0.1735 3300 0.0343 -
0.1761 3350 0.0095 -
0.1787 3400 0.0354 -
0.1814 3450 0.0085 -
0.1840 3500 0.001 -
0.1866 3550 0.0194 -
0.1893 3600 0.017 -
0.1919 3650 0.0003 -
0.1945 3700 0.0024 -
0.1972 3750 0.06 -
0.1998 3800 0.0006 -
0.2024 3850 0.0003 -
0.2050 3900 0.0311 -
0.2077 3950 0.023 -
0.2103 4000 0.0039 -
0.2129 4050 0.0085 -
0.2156 4100 0.0036 -
0.2182 4150 0.0015 -
0.2208 4200 0.0584 -
0.2234 4250 0.0004 -
0.2261 4300 0.0082 -
0.2287 4350 0.0001 -
0.2313 4400 0.0044 -
0.2340 4450 0.0003 -
0.2366 4500 0.0495 -
0.2392 4550 0.0073 -
0.2418 4600 0.0152 -
0.2445 4650 0.0033 -
0.2471 4700 0.0005 -
0.2497 4750 0.0102 -
0.2524 4800 0.046 -
0.2550 4850 0.0028 -
0.2576 4900 0.0014 -
0.2602 4950 0.0118 -
0.2629 5000 0.0042 -
0.2655 5050 0.0005 -
0.2681 5100 0.0031 -
0.2708 5150 0.0002 -
0.2734 5200 0.002 -
0.2760 5250 0.0111 -
0.2786 5300 0.0286 -
0.2813 5350 0.0009 -
0.2839 5400 0.0023 -
0.2865 5450 0.0079 -
0.2892 5500 0.0691 -
0.2918 5550 0.0403 -
0.2944 5600 0.0002 -
0.2970 5650 0.0057 -
0.2997 5700 0.0047 -
0.3023 5750 0.0322 -
0.3049 5800 0.0097 -
0.3076 5850 0.0012 -
0.3102 5900 0.0047 -
0.3128 5950 0.0925 -
0.3154 6000 0.0562 -
0.3181 6050 0.0058 -
0.3207 6100 0.0001 -
0.3233 6150 0.0029 -
0.3260 6200 0.0001 -
0.3286 6250 0.0035 -
0.3312 6300 0.0013 -
0.3338 6350 0.0152 -
0.3365 6400 0.0004 -
0.3391 6450 0.0114 -
0.3417 6500 0.0906 -
0.3444 6550 0.0005 -
0.3470 6600 0.0028 -
0.3496 6650 0.0395 -
0.3522 6700 0.0001 -
0.3549 6750 0.0044 -
0.3575 6800 0.0121 -
0.3601 6850 0.0012 -
0.3628 6900 0.0193 -
0.3654 6950 0.0014 -
0.3680 7000 0.0001 -
0.3706 7050 0.0618 -
0.3733 7100 0.0066 -
0.3759 7150 0.0426 -
0.3785 7200 0.0281 -
0.3812 7250 0.0254 -
0.3838 7300 0.0008 -
0.3864 7350 0.0047 -
0.3890 7400 0.0088 -
0.3917 7450 0.0004 -
0.3943 7500 0.0054 -
0.3969 7550 0.0371 -
0.3996 7600 0.0001 -
0.4022 7650 0.0082 -
0.4048 7700 0.0162 -
0.4074 7750 0.0093 -
0.4101 7800 0.0115 -
0.4127 7850 0.0114 -
0.4153 7900 0.0001 -
0.4180 7950 0.0002 -
0.4206 8000 0.0098 -
0.4232 8050 0.0001 -
0.4258 8100 0.0 -
0.4285 8150 0.0104 -
0.4311 8200 0.0564 -
0.4337 8250 0.0002 -
0.4364 8300 0.0176 -
0.4390 8350 0.0109 -
0.4416 8400 0.0001 -
0.4442 8450 0.0053 -
0.4469 8500 0.0629 -
0.4495 8550 0.0324 -
0.4521 8600 0.0003 -
0.4548 8650 0.0025 -
0.4574 8700 0.0032 -
0.4600 8750 0.0002 -
0.4626 8800 0.0001 -
0.4653 8850 0.0475 -
0.4679 8900 0.0114 -
0.4705 8950 0.0001 -
0.4732 9000 0.0028 -
0.4758 9050 0.0001 -
0.4784 9100 0.0002 -
0.4810 9150 0.0001 -
0.4837 9200 0.0001 -
0.4863 9250 0.0021 -
0.4889 9300 0.0001 -
0.4916 9350 0.0014 -
0.4942 9400 0.0176 -
0.4968 9450 0.0005 -
0.4994 9500 0.0001 -
0.5021 9550 0.0314 -
0.5047 9600 0.0613 -
0.5073 9650 0.018 -
0.5100 9700 0.0 -
0.5126 9750 0.0023 -
0.5152 9800 0.0013 -
0.5178 9850 0.0001 -
0.5205 9900 0.0003 -
0.5231 9950 0.001 -
0.5257 10000 0.0001 -
0.5284 10050 0.0193 -
0.5310 10100 0.0051 -
0.5336 10150 0.0001 -
0.5362 10200 0.0005 -
0.5389 10250 0.0 -
0.5415 10300 0.0001 -
0.5441 10350 0.0001 -
0.5468 10400 0.0037 -
0.5494 10450 0.0309 -
0.5520 10500 0.0286 -
0.5547 10550 0.0 -
0.5573 10600 0.0155 -
0.5599 10650 0.0001 -
0.5625 10700 0.0077 -
0.5652 10750 0.0153 -
0.5678 10800 0.0042 -
0.5704 10850 0.0103 -
0.5731 10900 0.0097 -
0.5757 10950 0.0109 -
0.5783 11000 0.0001 -
0.5809 11050 0.0103 -
0.5836 11100 0.0024 -
0.5862 11150 0.0001 -
0.5888 11200 0.0487 -
0.5915 11250 0.0009 -
0.5941 11300 0.0001 -
0.5967 11350 0.0002 -
0.5993 11400 0.0035 -
0.6020 11450 0.0005 -
0.6046 11500 0.0001 -
0.6072 11550 0.0049 -
0.6099 11600 0.0396 -
0.6125 11650 0.0177 -
0.6151 11700 0.0071 -
0.6177 11750 0.0071 -
0.6204 11800 0.0111 -
0.6230 11850 0.0145 -
0.6256 11900 0.037 -
0.6283 11950 0.0046 -
0.6309 12000 0.0258 -
0.6335 12050 0.0002 -
0.6361 12100 0.002 -
0.6388 12150 0.0119 -
0.6414 12200 0.0079 -
0.6440 12250 0.0239 -
0.6467 12300 0.0037 -
0.6493 12350 0.0366 -
0.6519 12400 0.0201 -
0.6545 12450 0.002 -
0.6572 12500 0.0652 -
0.6598 12550 0.005 -
0.6624 12600 0.0034 -
0.6651 12650 0.0003 -
0.6677 12700 0.0022 -
0.6703 12750 0.0001 -
0.6729 12800 0.0175 -
0.6756 12850 0.0003 -
0.6782 12900 0.0085 -
0.6808 12950 0.0036 -
0.6835 13000 0.0 -
0.6861 13050 0.0097 -
0.6887 13100 0.006 -
0.6913 13150 0.0001 -
0.6940 13200 0.0001 -
0.6966 13250 0.0379 -
0.6992 13300 0.0076 -
0.7019 13350 0.0627 -
0.7045 13400 0.0605 -
0.7071 13450 0.0081 -
0.7097 13500 0.0018 -
0.7124 13550 0.018 -
0.7150 13600 0.0035 -
0.7176 13650 0.0001 -
0.7203 13700 0.0001 -
0.7229 13750 0.0507 -
0.7255 13800 0.0082 -
0.7281 13850 0.0082 -
0.7308 13900 0.0106 -
0.7334 13950 0.0067 -
0.7360 14000 0.0062 -
0.7387 14050 0.0001 -
0.7413 14100 0.0246 -
0.7439 14150 0.0033 -
0.7465 14200 0.0001 -
0.7492 14250 0.0432 -
0.7518 14300 0.0502 -
0.7544 14350 0.0079 -
0.7571 14400 0.0291 -
0.7597 14450 0.0002 -
0.7623 14500 0.0029 -
0.7649 14550 0.0321 -
0.7676 14600 0.0002 -
0.7702 14650 0.0053 -
0.7728 14700 0.0094 -
0.7755 14750 0.0156 -
0.7781 14800 0.071 -
0.7807 14850 0.0001 -
0.7833 14900 0.0037 -
0.7860 14950 0.0544 -
0.7886 15000 0.0034 -
0.7912 15050 0.0018 -
0.7939 15100 0.0014 -
0.7965 15150 0.0189 -
0.7991 15200 0.0001 -
0.8017 15250 0.0057 -
0.8044 15300 0.0001 -
0.8070 15350 0.0002 -
0.8096 15400 0.0003 -
0.8123 15450 0.0006 -
0.8149 15500 0.1085 -
0.8175 15550 0.0003 -
0.8201 15600 0.0001 -
0.8228 15650 0.0005 -
0.8254 15700 0.014 -
0.8280 15750 0.0036 -
0.8307 15800 0.0001 -
0.8333 15850 0.0 -
0.8359 15900 0.0 -
0.8385 15950 0.0001 -
0.8412 16000 0.0001 -
0.8438 16050 0.0271 -
0.8464 16100 0.0093 -
0.8491 16150 0.0444 -
0.8517 16200 0.0002 -
0.8543 16250 0.0007 -
0.8569 16300 0.0002 -
0.8596 16350 0.0012 -
0.8622 16400 0.0 -
0.8648 16450 0.0177 -
0.8675 16500 0.0342 -
0.8701 16550 0.0288 -
0.8727 16600 0.0 -
0.8753 16650 0.0024 -
0.8780 16700 0.0003 -
0.8806 16750 0.0063 -
0.8832 16800 0.0442 -
0.8859 16850 0.0092 -
0.8885 16900 0.0089 -
0.8911 16950 0.0027 -
0.8937 17000 0.0521 -
0.8964 17050 0.0023 -
0.8990 17100 0.051 -
0.9016 17150 0.0015 -
0.9043 17200 0.0003 -
0.9069 17250 0.0177 -
0.9095 17300 0.0031 -
0.9121 17350 0.0205 -
0.9148 17400 0.0172 -
0.9174 17450 0.0001 -
0.9200 17500 0.005 -
0.9227 17550 0.0409 -
0.9253 17600 0.0001 -
0.9279 17650 0.0 -
0.9306 17700 0.0002 -
0.9332 17750 0.0274 -
0.9358 17800 0.0077 -
0.9384 17850 0.0078 -
0.9411 17900 0.0001 -
0.9437 17950 0.0 -
0.9463 18000 0.0437 -
0.9490 18050 0.0143 -
0.9516 18100 0.001 -
0.9542 18150 0.0001 -
0.9568 18200 0.0428 -
0.9595 18250 0.0036 -
0.9621 18300 0.0001 -
0.9647 18350 0.0001 -
0.9674 18400 0.0063 -
0.9700 18450 0.0 -
0.9726 18500 0.0196 -
0.9752 18550 0.0001 -
0.9779 18600 0.0001 -
0.9805 18650 0.0001 -
0.9831 18700 0.0397 -
0.9858 18750 0.008 -
0.9884 18800 0.015 -
0.9910 18850 0.0 -
0.9936 18900 0.003 -
0.9963 18950 0.025 -
0.9989 19000 0.003 -
1.0 19021 - 0.2343
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.4.0
  • Transformers: 4.38.1
  • PyTorch: 2.1.0+cu118
  • Datasets: 2.17.1
  • 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
5
Safetensors
Model size
278M params
Tensor type
F32
·
Inference Examples
Text Classification
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for luis-cardoso-q/kotodama-multilingual-v3

Base model

sentence-transformers/paraphrase-multilingual-mpnet-base-v2
Finetuned
(42)
this model

Evaluation results

View on Papers With Code