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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
metrics:
- accuracy
widget:
- text: 'loan repayment '
- text: 2023-F48
- text: 'acompte '
- text: 2023-12-1165548
- text: Facture 20230040
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.73568281938326
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 7 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------------------|:---------------------------------------------------------------------------------------------------------------------------------|
| company name | <ul><li>'Werbeverguetung Ford Mustang GT Cabrio Mueller Marc'</li><li>'ELEVEN PARIS'</li><li>'Saldo quota iscrizione '</li></ul> |
| salary | <ul><li>'stipendio gianoli gen 23'</li><li>'Salaire Fevrier'</li><li>'salaire'</li></ul> |
| invoice | <ul><li>'PAIEMENT DE FACTURE FAT00014728'</li><li>'fattura 3/24'</li><li>'saldo fattura'</li></ul> |
| random characters | <ul><li>'arretrati'</li><li>'Identnummer 00480257'</li><li>'CEP AGENCE'</li></ul> |
| refund | <ul><li>'versement'</li><li>'Remboursement frais transport'</li><li>'REMB FRAIS V / V LONDRES'</li></ul> |
| buying | <ul><li>'voiture'</li><li>'fourniture'</li><li>'fournisseur '</li></ul> |
| rent | <ul><li>'loyer'</li><li>'loyer mars 2023'</li><li>'loyer'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.7357 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("luis-cardoso-q/kotodama-multilingual-v3")
# Run inference
preds = model("2023-F48")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## 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
```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}
}
```
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