---
base_model: intfloat/multilingual-e5-large
library_name: setfit
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 'men det kan så åbne nogle nye
'
- text: 'som jeg siger, der jo en grund til at jeg har fået et
handicapskilt og sådan'
- text: 'meget, ellers har jeg overholdt alt.
'
- text: 'sige det er 15 timer, du får betalte timer, jamen det er også en start,
'
- text: og jo det er nok rigtigt, det er sådan, jeg skal gøre det
inference: true
model-index:
- name: SetFit with intfloat/multilingual-e5-large
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9724770642201835
name: Accuracy
- type: precision
value: 0.9557522123893806
name: Precision
- type: recall
value: 0.9908256880733946
name: Recall
- type: f1
value: 0.972972972972973
name: F1
---
# SetFit with intfloat/multilingual-e5-large
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) 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:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
### 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 |
|:--------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| reported speech | - 'der fortalte jeg dig alt det der om ret og pligt '
- 'Så havde vi de der snakke om, at du ligesom selv fik lov at styre dit\nNem-ID.'
- 'Amina sagde at min\ndumme mor havde ringet'
|
| not reported speech | - 'sag. '
- 'du klage over ik Lykke?\n\n'
- 'S: nej. '
|
## Evaluation
### Metrics
| Label | Accuracy | Precision | Recall | F1 |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.9725 | 0.9558 | 0.9908 | 0.9730 |
## 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("setfit_model_id")
# Run inference
preds = model("men det kan så åbne nogle nye
")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 19.1755 | 196 |
| Label | Training Sample Count |
|:--------------------|:----------------------|
| not reported speech | 265 |
| reported speech | 265 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (6, 6)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (1.0770502781075495e-06, 1.0770502781075495e-06)
- 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 |
|:-------:|:---------:|:-------------:|:---------------:|
| 0.0002 | 1 | 0.3495 | - |
| 0.0113 | 50 | 0.3524 | - |
| 0.0227 | 100 | 0.3496 | - |
| 0.0340 | 150 | 0.3464 | - |
| 0.0454 | 200 | 0.3419 | - |
| 0.0567 | 250 | 0.328 | - |
| 0.0681 | 300 | 0.3166 | - |
| 0.0794 | 350 | 0.3012 | - |
| 0.0908 | 400 | 0.277 | - |
| 0.1021 | 450 | 0.259 | - |
| 0.1135 | 500 | 0.2568 | - |
| 0.1248 | 550 | 0.2483 | - |
| 0.1362 | 600 | 0.2457 | - |
| 0.1475 | 650 | 0.2263 | - |
| 0.1589 | 700 | 0.2361 | - |
| 0.1702 | 750 | 0.2108 | - |
| 0.1816 | 800 | 0.2025 | - |
| 0.1929 | 850 | 0.1881 | - |
| 0.2043 | 900 | 0.1559 | - |
| 0.2156 | 950 | 0.1055 | - |
| 0.2270 | 1000 | 0.0693 | - |
| 0.2383 | 1050 | 0.0332 | - |
| 0.2497 | 1100 | 0.0287 | - |
| 0.2610 | 1150 | 0.0185 | - |
| 0.2724 | 1200 | 0.0421 | - |
| 0.2837 | 1250 | 0.0087 | - |
| 0.2951 | 1300 | 0.0233 | - |
| 0.3064 | 1350 | 0.0083 | - |
| 0.3177 | 1400 | 0.0043 | - |
| 0.3291 | 1450 | 0.0037 | - |
| 0.3404 | 1500 | 0.0033 | - |
| 0.3518 | 1550 | 0.0019 | - |
| 0.3631 | 1600 | 0.0016 | - |
| 0.3745 | 1650 | 0.0012 | - |
| 0.3858 | 1700 | 0.002 | - |
| 0.3972 | 1750 | 0.0014 | - |
| 0.4085 | 1800 | 0.0012 | - |
| 0.4199 | 1850 | 0.001 | - |
| 0.4312 | 1900 | 0.001 | - |
| 0.4426 | 1950 | 0.0037 | - |
| 0.4539 | 2000 | 0.0006 | - |
| 0.4653 | 2050 | 0.0009 | - |
| 0.4766 | 2100 | 0.001 | - |
| 0.4880 | 2150 | 0.0006 | - |
| 0.4993 | 2200 | 0.0007 | - |
| 0.5107 | 2250 | 0.0005 | - |
| 0.5220 | 2300 | 0.001 | - |
| 0.5334 | 2350 | 0.0006 | - |
| 0.5447 | 2400 | 0.0004 | - |
| 0.5561 | 2450 | 0.0003 | - |
| 0.5674 | 2500 | 0.0004 | - |
| 0.5788 | 2550 | 0.0005 | - |
| 0.5901 | 2600 | 0.0003 | - |
| 0.6015 | 2650 | 0.0003 | - |
| 0.6128 | 2700 | 0.0003 | - |
| 0.6241 | 2750 | 0.0003 | - |
| 0.6355 | 2800 | 0.0004 | - |
| 0.6468 | 2850 | 0.0003 | - |
| 0.6582 | 2900 | 0.0002 | - |
| 0.6695 | 2950 | 0.0003 | - |
| 0.6809 | 3000 | 0.0003 | - |
| 0.6922 | 3050 | 0.0003 | - |
| 0.7036 | 3100 | 0.0003 | - |
| 0.7149 | 3150 | 0.0002 | - |
| 0.7263 | 3200 | 0.0003 | - |
| 0.7376 | 3250 | 0.0001 | - |
| 0.7490 | 3300 | 0.0002 | - |
| 0.7603 | 3350 | 0.0002 | - |
| 0.7717 | 3400 | 0.0002 | - |
| 0.7830 | 3450 | 0.0002 | - |
| 0.7944 | 3500 | 0.0002 | - |
| 0.8057 | 3550 | 0.0002 | - |
| 0.8171 | 3600 | 0.0002 | - |
| 0.8284 | 3650 | 0.0002 | - |
| 0.8398 | 3700 | 0.0003 | - |
| 0.8511 | 3750 | 0.0002 | - |
| 0.8625 | 3800 | 0.0002 | - |
| 0.8738 | 3850 | 0.0002 | - |
| 0.8852 | 3900 | 0.0002 | - |
| 0.8965 | 3950 | 0.0002 | - |
| 0.9079 | 4000 | 0.0001 | - |
| 0.9192 | 4050 | 0.0002 | - |
| 0.9305 | 4100 | 0.0002 | - |
| 0.9419 | 4150 | 0.0001 | - |
| 0.9532 | 4200 | 0.0001 | - |
| 0.9646 | 4250 | 0.0001 | - |
| 0.9759 | 4300 | 0.0001 | - |
| 0.9873 | 4350 | 0.0002 | - |
| 0.9986 | 4400 | 0.0002 | - |
| 1.0 | 4406 | - | 0.0601 |
| 1.0100 | 4450 | 0.0001 | - |
| 1.0213 | 4500 | 0.0001 | - |
| 1.0327 | 4550 | 0.0001 | - |
| 1.0440 | 4600 | 0.0001 | - |
| 1.0554 | 4650 | 0.0001 | - |
| 1.0667 | 4700 | 0.0001 | - |
| 1.0781 | 4750 | 0.0001 | - |
| 1.0894 | 4800 | 0.0001 | - |
| 1.1008 | 4850 | 0.0001 | - |
| 1.1121 | 4900 | 0.0001 | - |
| 1.1235 | 4950 | 0.0001 | - |
| 1.1348 | 5000 | 0.0001 | - |
| 1.1462 | 5050 | 0.0002 | - |
| 1.1575 | 5100 | 0.0001 | - |
| 1.1689 | 5150 | 0.0001 | - |
| 1.1802 | 5200 | 0.0001 | - |
| 1.1916 | 5250 | 0.0001 | - |
| 1.2029 | 5300 | 0.0001 | - |
| 1.2143 | 5350 | 0.0001 | - |
| 1.2256 | 5400 | 0.0001 | - |
| 1.2369 | 5450 | 0.0001 | - |
| 1.2483 | 5500 | 0.0001 | - |
| 1.2596 | 5550 | 0.0001 | - |
| 1.2710 | 5600 | 0.0001 | - |
| 1.2823 | 5650 | 0.0001 | - |
| 1.2937 | 5700 | 0.0001 | - |
| 1.3050 | 5750 | 0.0001 | - |
| 1.3164 | 5800 | 0.0001 | - |
| 1.3277 | 5850 | 0.0001 | - |
| 1.3391 | 5900 | 0.0001 | - |
| 1.3504 | 5950 | 0.0001 | - |
| 1.3618 | 6000 | 0.0001 | - |
| 1.3731 | 6050 | 0.0001 | - |
| 1.3845 | 6100 | 0.0001 | - |
| 1.3958 | 6150 | 0.0001 | - |
| 1.4072 | 6200 | 0.0001 | - |
| 1.4185 | 6250 | 0.0001 | - |
| 1.4299 | 6300 | 0.0001 | - |
| 1.4412 | 6350 | 0.0001 | - |
| 1.4526 | 6400 | 0.0001 | - |
| 1.4639 | 6450 | 0.0 | - |
| 1.4753 | 6500 | 0.0001 | - |
| 1.4866 | 6550 | 0.0011 | - |
| 1.4980 | 6600 | 0.0001 | - |
| 1.5093 | 6650 | 0.0001 | - |
| 1.5207 | 6700 | 0.0 | - |
| 1.5320 | 6750 | 0.0 | - |
| 1.5433 | 6800 | 0.0001 | - |
| 1.5547 | 6850 | 0.0001 | - |
| 1.5660 | 6900 | 0.0001 | - |
| 1.5774 | 6950 | 0.0001 | - |
| 1.5887 | 7000 | 0.0001 | - |
| 1.6001 | 7050 | 0.0001 | - |
| 1.6114 | 7100 | 0.0001 | - |
| 1.6228 | 7150 | 0.0 | - |
| 1.6341 | 7200 | 0.0 | - |
| 1.6455 | 7250 | 0.0001 | - |
| 1.6568 | 7300 | 0.0001 | - |
| 1.6682 | 7350 | 0.0001 | - |
| 1.6795 | 7400 | 0.0001 | - |
| 1.6909 | 7450 | 0.0 | - |
| 1.7022 | 7500 | 0.0001 | - |
| 1.7136 | 7550 | 0.0001 | - |
| 1.7249 | 7600 | 0.0001 | - |
| 1.7363 | 7650 | 0.0 | - |
| 1.7476 | 7700 | 0.0 | - |
| 1.7590 | 7750 | 0.0 | - |
| 1.7703 | 7800 | 0.0001 | - |
| 1.7817 | 7850 | 0.0 | - |
| 1.7930 | 7900 | 0.0 | - |
| 1.8044 | 7950 | 0.0 | - |
| 1.8157 | 8000 | 0.0001 | - |
| 1.8271 | 8050 | 0.0 | - |
| 1.8384 | 8100 | 0.0 | - |
| 1.8498 | 8150 | 0.0001 | - |
| 1.8611 | 8200 | 0.0 | - |
| 1.8724 | 8250 | 0.0001 | - |
| 1.8838 | 8300 | 0.0 | - |
| 1.8951 | 8350 | 0.0001 | - |
| 1.9065 | 8400 | 0.0001 | - |
| 1.9178 | 8450 | 0.0001 | - |
| 1.9292 | 8500 | 0.0 | - |
| 1.9405 | 8550 | 0.0 | - |
| 1.9519 | 8600 | 0.0 | - |
| 1.9632 | 8650 | 0.0 | - |
| 1.9746 | 8700 | 0.0 | - |
| 1.9859 | 8750 | 0.0 | - |
| 1.9973 | 8800 | 0.0 | - |
| 2.0 | 8812 | - | 0.0549 |
| 2.0086 | 8850 | 0.0 | - |
| 2.0200 | 8900 | 0.0 | - |
| 2.0313 | 8950 | 0.0 | - |
| 2.0427 | 9000 | 0.0 | - |
| 2.0540 | 9050 | 0.0 | - |
| 2.0654 | 9100 | 0.0 | - |
| 2.0767 | 9150 | 0.0 | - |
| 2.0881 | 9200 | 0.0 | - |
| 2.0994 | 9250 | 0.0 | - |
| 2.1108 | 9300 | 0.0 | - |
| 2.1221 | 9350 | 0.0 | - |
| 2.1335 | 9400 | 0.0001 | - |
| 2.1448 | 9450 | 0.0 | - |
| 2.1562 | 9500 | 0.0 | - |
| 2.1675 | 9550 | 0.0 | - |
| 2.1788 | 9600 | 0.0 | - |
| 2.1902 | 9650 | 0.0 | - |
| 2.2015 | 9700 | 0.0 | - |
| 2.2129 | 9750 | 0.0 | - |
| 2.2242 | 9800 | 0.0 | - |
| 2.2356 | 9850 | 0.0 | - |
| 2.2469 | 9900 | 0.0 | - |
| 2.2583 | 9950 | 0.0 | - |
| 2.2696 | 10000 | 0.0 | - |
| 2.2810 | 10050 | 0.0 | - |
| 2.2923 | 10100 | 0.0 | - |
| 2.3037 | 10150 | 0.0 | - |
| 2.3150 | 10200 | 0.0 | - |
| 2.3264 | 10250 | 0.0 | - |
| 2.3377 | 10300 | 0.0 | - |
| 2.3491 | 10350 | 0.0 | - |
| 2.3604 | 10400 | 0.0 | - |
| 2.3718 | 10450 | 0.0001 | - |
| 2.3831 | 10500 | 0.0 | - |
| 2.3945 | 10550 | 0.0 | - |
| 2.4058 | 10600 | 0.0 | - |
| 2.4172 | 10650 | 0.0 | - |
| 2.4285 | 10700 | 0.0 | - |
| 2.4399 | 10750 | 0.0 | - |
| 2.4512 | 10800 | 0.0 | - |
| 2.4626 | 10850 | 0.0 | - |
| 2.4739 | 10900 | 0.0 | - |
| 2.4852 | 10950 | 0.0 | - |
| 2.4966 | 11000 | 0.0 | - |
| 2.5079 | 11050 | 0.0 | - |
| 2.5193 | 11100 | 0.0 | - |
| 2.5306 | 11150 | 0.0 | - |
| 2.5420 | 11200 | 0.0 | - |
| 2.5533 | 11250 | 0.0 | - |
| 2.5647 | 11300 | 0.0 | - |
| 2.5760 | 11350 | 0.0 | - |
| 2.5874 | 11400 | 0.0 | - |
| 2.5987 | 11450 | 0.0 | - |
| 2.6101 | 11500 | 0.0 | - |
| 2.6214 | 11550 | 0.0 | - |
| 2.6328 | 11600 | 0.0 | - |
| 2.6441 | 11650 | 0.0 | - |
| 2.6555 | 11700 | 0.0 | - |
| 2.6668 | 11750 | 0.0 | - |
| 2.6782 | 11800 | 0.0 | - |
| 2.6895 | 11850 | 0.0 | - |
| 2.7009 | 11900 | 0.0 | - |
| 2.7122 | 11950 | 0.0 | - |
| 2.7236 | 12000 | 0.0 | - |
| 2.7349 | 12050 | 0.0 | - |
| 2.7463 | 12100 | 0.0 | - |
| 2.7576 | 12150 | 0.0 | - |
| 2.7690 | 12200 | 0.0 | - |
| 2.7803 | 12250 | 0.0 | - |
| 2.7916 | 12300 | 0.0 | - |
| 2.8030 | 12350 | 0.0 | - |
| 2.8143 | 12400 | 0.0 | - |
| 2.8257 | 12450 | 0.0 | - |
| 2.8370 | 12500 | 0.0 | - |
| 2.8484 | 12550 | 0.0 | - |
| 2.8597 | 12600 | 0.0 | - |
| 2.8711 | 12650 | 0.0 | - |
| 2.8824 | 12700 | 0.0 | - |
| 2.8938 | 12750 | 0.0 | - |
| 2.9051 | 12800 | 0.0 | - |
| 2.9165 | 12850 | 0.0 | - |
| 2.9278 | 12900 | 0.0 | - |
| 2.9392 | 12950 | 0.0 | - |
| 2.9505 | 13000 | 0.0 | - |
| 2.9619 | 13050 | 0.0 | - |
| 2.9732 | 13100 | 0.0 | - |
| 2.9846 | 13150 | 0.0 | - |
| 2.9959 | 13200 | 0.0 | - |
| 3.0 | 13218 | - | 0.0469 |
| 3.0073 | 13250 | 0.0 | - |
| 3.0186 | 13300 | 0.0 | - |
| 3.0300 | 13350 | 0.0 | - |
| 3.0413 | 13400 | 0.0 | - |
| 3.0527 | 13450 | 0.0 | - |
| 3.0640 | 13500 | 0.0 | - |
| 3.0754 | 13550 | 0.0 | - |
| 3.0867 | 13600 | 0.0 | - |
| 3.0980 | 13650 | 0.0 | - |
| 3.1094 | 13700 | 0.0 | - |
| 3.1207 | 13750 | 0.0 | - |
| 3.1321 | 13800 | 0.0 | - |
| 3.1434 | 13850 | 0.0 | - |
| 3.1548 | 13900 | 0.0 | - |
| 3.1661 | 13950 | 0.0 | - |
| 3.1775 | 14000 | 0.0 | - |
| 3.1888 | 14050 | 0.0 | - |
| 3.2002 | 14100 | 0.0 | - |
| 3.2115 | 14150 | 0.0 | - |
| 3.2229 | 14200 | 0.0 | - |
| 3.2342 | 14250 | 0.0 | - |
| 3.2456 | 14300 | 0.0 | - |
| 3.2569 | 14350 | 0.0 | - |
| 3.2683 | 14400 | 0.0 | - |
| 3.2796 | 14450 | 0.0 | - |
| 3.2910 | 14500 | 0.0 | - |
| 3.3023 | 14550 | 0.0 | - |
| 3.3137 | 14600 | 0.0 | - |
| 3.3250 | 14650 | 0.0 | - |
| 3.3364 | 14700 | 0.0 | - |
| 3.3477 | 14750 | 0.0 | - |
| 3.3591 | 14800 | 0.0 | - |
| 3.3704 | 14850 | 0.0 | - |
| 3.3818 | 14900 | 0.0 | - |
| 3.3931 | 14950 | 0.0 | - |
| 3.4044 | 15000 | 0.0 | - |
| 3.4158 | 15050 | 0.0 | - |
| 3.4271 | 15100 | 0.0 | - |
| 3.4385 | 15150 | 0.0 | - |
| 3.4498 | 15200 | 0.0 | - |
| 3.4612 | 15250 | 0.0 | - |
| 3.4725 | 15300 | 0.0 | - |
| 3.4839 | 15350 | 0.0 | - |
| 3.4952 | 15400 | 0.0 | - |
| 3.5066 | 15450 | 0.0 | - |
| 3.5179 | 15500 | 0.0 | - |
| 3.5293 | 15550 | 0.0 | - |
| 3.5406 | 15600 | 0.0 | - |
| 3.5520 | 15650 | 0.0 | - |
| 3.5633 | 15700 | 0.0 | - |
| 3.5747 | 15750 | 0.0 | - |
| 3.5860 | 15800 | 0.0 | - |
| 3.5974 | 15850 | 0.0 | - |
| 3.6087 | 15900 | 0.0 | - |
| 3.6201 | 15950 | 0.0 | - |
| 3.6314 | 16000 | 0.0 | - |
| 3.6428 | 16050 | 0.0 | - |
| 3.6541 | 16100 | 0.0 | - |
| 3.6655 | 16150 | 0.0 | - |
| 3.6768 | 16200 | 0.0 | - |
| 3.6882 | 16250 | 0.0 | - |
| 3.6995 | 16300 | 0.0 | - |
| 3.7108 | 16350 | 0.0 | - |
| 3.7222 | 16400 | 0.0 | - |
| 3.7335 | 16450 | 0.0 | - |
| 3.7449 | 16500 | 0.0 | - |
| 3.7562 | 16550 | 0.0 | - |
| 3.7676 | 16600 | 0.0 | - |
| 3.7789 | 16650 | 0.0 | - |
| 3.7903 | 16700 | 0.0 | - |
| 3.8016 | 16750 | 0.0 | - |
| 3.8130 | 16800 | 0.0 | - |
| 3.8243 | 16850 | 0.0 | - |
| 3.8357 | 16900 | 0.0 | - |
| 3.8470 | 16950 | 0.0 | - |
| 3.8584 | 17000 | 0.0 | - |
| 3.8697 | 17050 | 0.0 | - |
| 3.8811 | 17100 | 0.0 | - |
| 3.8924 | 17150 | 0.0 | - |
| 3.9038 | 17200 | 0.0 | - |
| 3.9151 | 17250 | 0.0 | - |
| 3.9265 | 17300 | 0.0 | - |
| 3.9378 | 17350 | 0.0 | - |
| 3.9492 | 17400 | 0.0 | - |
| 3.9605 | 17450 | 0.0 | - |
| 3.9719 | 17500 | 0.0 | - |
| 3.9832 | 17550 | 0.0 | - |
| 3.9946 | 17600 | 0.0 | - |
| 4.0 | 17624 | - | 0.0404 |
| 4.0059 | 17650 | 0.0 | - |
| 4.0172 | 17700 | 0.0 | - |
| 4.0286 | 17750 | 0.0 | - |
| 4.0399 | 17800 | 0.0 | - |
| 4.0513 | 17850 | 0.0 | - |
| 4.0626 | 17900 | 0.0 | - |
| 4.0740 | 17950 | 0.0 | - |
| 4.0853 | 18000 | 0.0 | - |
| 4.0967 | 18050 | 0.0 | - |
| 4.1080 | 18100 | 0.0 | - |
| 4.1194 | 18150 | 0.0 | - |
| 4.1307 | 18200 | 0.0 | - |
| 4.1421 | 18250 | 0.0 | - |
| 4.1534 | 18300 | 0.0 | - |
| 4.1648 | 18350 | 0.0 | - |
| 4.1761 | 18400 | 0.0 | - |
| 4.1875 | 18450 | 0.0 | - |
| 4.1988 | 18500 | 0.0 | - |
| 4.2102 | 18550 | 0.0 | - |
| 4.2215 | 18600 | 0.0 | - |
| 4.2329 | 18650 | 0.0 | - |
| 4.2442 | 18700 | 0.0 | - |
| 4.2556 | 18750 | 0.0 | - |
| 4.2669 | 18800 | 0.0 | - |
| 4.2783 | 18850 | 0.0 | - |
| 4.2896 | 18900 | 0.0 | - |
| 4.3010 | 18950 | 0.0 | - |
| 4.3123 | 19000 | 0.0 | - |
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| 4.3350 | 19100 | 0.0 | - |
| 4.3463 | 19150 | 0.0 | - |
| 4.3577 | 19200 | 0.0 | - |
| 4.3690 | 19250 | 0.0 | - |
| 4.3804 | 19300 | 0.0 | - |
| 4.3917 | 19350 | 0.0 | - |
| 4.4031 | 19400 | 0.0 | - |
| 4.4144 | 19450 | 0.0 | - |
| 4.4258 | 19500 | 0.0 | - |
| 4.4371 | 19550 | 0.0 | - |
| 4.4485 | 19600 | 0.0 | - |
| 4.4598 | 19650 | 0.0 | - |
| 4.4712 | 19700 | 0.0 | - |
| 4.4825 | 19750 | 0.0 | - |
| 4.4939 | 19800 | 0.0 | - |
| 4.5052 | 19850 | 0.0 | - |
| 4.5166 | 19900 | 0.0 | - |
| 4.5279 | 19950 | 0.0 | - |
| 4.5393 | 20000 | 0.0 | - |
| 4.5506 | 20050 | 0.0 | - |
| 4.5620 | 20100 | 0.0 | - |
| 4.5733 | 20150 | 0.0 | - |
| 4.5847 | 20200 | 0.0 | - |
| 4.5960 | 20250 | 0.0 | - |
| 4.6074 | 20300 | 0.0 | - |
| 4.6187 | 20350 | 0.0 | - |
| 4.6300 | 20400 | 0.0 | - |
| 4.6414 | 20450 | 0.0 | - |
| 4.6527 | 20500 | 0.0 | - |
| 4.6641 | 20550 | 0.0 | - |
| 4.6754 | 20600 | 0.0 | - |
| 4.6868 | 20650 | 0.0 | - |
| 4.6981 | 20700 | 0.0 | - |
| 4.7095 | 20750 | 0.0 | - |
| 4.7208 | 20800 | 0.0 | - |
| 4.7322 | 20850 | 0.0 | - |
| 4.7435 | 20900 | 0.0 | - |
| 4.7549 | 20950 | 0.0 | - |
| 4.7662 | 21000 | 0.0 | - |
| 4.7776 | 21050 | 0.0 | - |
| 4.7889 | 21100 | 0.0 | - |
| 4.8003 | 21150 | 0.0 | - |
| 4.8116 | 21200 | 0.0 | - |
| 4.8230 | 21250 | 0.0 | - |
| 4.8343 | 21300 | 0.0 | - |
| 4.8457 | 21350 | 0.0 | - |
| 4.8570 | 21400 | 0.0 | - |
| 4.8684 | 21450 | 0.0 | - |
| 4.8797 | 21500 | 0.0 | - |
| 4.8911 | 21550 | 0.0 | - |
| 4.9024 | 21600 | 0.0 | - |
| 4.9138 | 21650 | 0.0 | - |
| 4.9251 | 21700 | 0.0 | - |
| 4.9365 | 21750 | 0.0 | - |
| 4.9478 | 21800 | 0.0 | - |
| 4.9591 | 21850 | 0.0 | - |
| 4.9705 | 21900 | 0.0 | - |
| 4.9818 | 21950 | 0.0 | - |
| 4.9932 | 22000 | 0.0 | - |
| **5.0** | **22030** | **-** | **0.038** |
| 5.0045 | 22050 | 0.0 | - |
| 5.0159 | 22100 | 0.0 | - |
| 5.0272 | 22150 | 0.0 | - |
| 5.0386 | 22200 | 0.0 | - |
| 5.0499 | 22250 | 0.0 | - |
| 5.0613 | 22300 | 0.0 | - |
| 5.0726 | 22350 | 0.0 | - |
| 5.0840 | 22400 | 0.0 | - |
| 5.0953 | 22450 | 0.0 | - |
| 5.1067 | 22500 | 0.0 | - |
| 5.1180 | 22550 | 0.0 | - |
| 5.1294 | 22600 | 0.0 | - |
| 5.1407 | 22650 | 0.0 | - |
| 5.1521 | 22700 | 0.0 | - |
| 5.1634 | 22750 | 0.0 | - |
| 5.1748 | 22800 | 0.0 | - |
| 5.1861 | 22850 | 0.0 | - |
| 5.1975 | 22900 | 0.0 | - |
| 5.2088 | 22950 | 0.0 | - |
| 5.2202 | 23000 | 0.0 | - |
| 5.2315 | 23050 | 0.0 | - |
| 5.2429 | 23100 | 0.0 | - |
| 5.2542 | 23150 | 0.0 | - |
| 5.2655 | 23200 | 0.0 | - |
| 5.2769 | 23250 | 0.0 | - |
| 5.2882 | 23300 | 0.0 | - |
| 5.2996 | 23350 | 0.0 | - |
| 5.3109 | 23400 | 0.0 | - |
| 5.3223 | 23450 | 0.0 | - |
| 5.3336 | 23500 | 0.0 | - |
| 5.3450 | 23550 | 0.0 | - |
| 5.3563 | 23600 | 0.0 | - |
| 5.3677 | 23650 | 0.0 | - |
| 5.3790 | 23700 | 0.0 | - |
| 5.3904 | 23750 | 0.0 | - |
| 5.4017 | 23800 | 0.0 | - |
| 5.4131 | 23850 | 0.0 | - |
| 5.4244 | 23900 | 0.0 | - |
| 5.4358 | 23950 | 0.0 | - |
| 5.4471 | 24000 | 0.0 | - |
| 5.4585 | 24050 | 0.0 | - |
| 5.4698 | 24100 | 0.0 | - |
| 5.4812 | 24150 | 0.0 | - |
| 5.4925 | 24200 | 0.0 | - |
| 5.5039 | 24250 | 0.0 | - |
| 5.5152 | 24300 | 0.0 | - |
| 5.5266 | 24350 | 0.0 | - |
| 5.5379 | 24400 | 0.0 | - |
| 5.5493 | 24450 | 0.0 | - |
| 5.5606 | 24500 | 0.0 | - |
| 5.5719 | 24550 | 0.0 | - |
| 5.5833 | 24600 | 0.0 | - |
| 5.5946 | 24650 | 0.0 | - |
| 5.6060 | 24700 | 0.0 | - |
| 5.6173 | 24750 | 0.0 | - |
| 5.6287 | 24800 | 0.0 | - |
| 5.6400 | 24850 | 0.0 | - |
| 5.6514 | 24900 | 0.0 | - |
| 5.6627 | 24950 | 0.0 | - |
| 5.6741 | 25000 | 0.0 | - |
| 5.6854 | 25050 | 0.0 | - |
| 5.6968 | 25100 | 0.0 | - |
| 5.7081 | 25150 | 0.0 | - |
| 5.7195 | 25200 | 0.0 | - |
| 5.7308 | 25250 | 0.0 | - |
| 5.7422 | 25300 | 0.0 | - |
| 5.7535 | 25350 | 0.0 | - |
| 5.7649 | 25400 | 0.0 | - |
| 5.7762 | 25450 | 0.0 | - |
| 5.7876 | 25500 | 0.0 | - |
| 5.7989 | 25550 | 0.0 | - |
| 5.8103 | 25600 | 0.0 | - |
| 5.8216 | 25650 | 0.0 | - |
| 5.8330 | 25700 | 0.0 | - |
| 5.8443 | 25750 | 0.0 | - |
| 5.8557 | 25800 | 0.0 | - |
| 5.8670 | 25850 | 0.0 | - |
| 5.8783 | 25900 | 0.0 | - |
| 5.8897 | 25950 | 0.0 | - |
| 5.9010 | 26000 | 0.0 | - |
| 5.9124 | 26050 | 0.0 | - |
| 5.9237 | 26100 | 0.0 | - |
| 5.9351 | 26150 | 0.0 | - |
| 5.9464 | 26200 | 0.0 | - |
| 5.9578 | 26250 | 0.0 | - |
| 5.9691 | 26300 | 0.0 | - |
| 5.9805 | 26350 | 0.0 | - |
| 5.9918 | 26400 | 0.0 | - |
| 6.0 | 26436 | - | 0.0382 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.3
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.4.1+cu121
- Datasets: 2.21.0
- 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}
}
```