---
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)

## 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->