---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Good morning
- text: how does the recommendation system work on this platform
- text: who are you
- text: where is the search bar
- text: how can I find courses related to programming
inference: true
model-index:
- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8333333333333334
name: Accuracy
---
# SetFit with sentence-transformers/all-MiniLM-L6-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 6 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 |
|:--------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| general-questions | - 'can you explain the concept of cloud computing'
- 'how do I assess my skills after completing a course'
- 'what is the significance of feedback in online learning'
|
| website-information | - 'how to access the dashboard'
- 'where can I see my completed courses'
- 'where can I find notifications'
|
| greet-who_are_you | - "pourquoi j'ai besoin de toi"
- 'help please'
- 'I can not understand you'
|
| recommendations | - 'how do I get recommendations based on my interests'
- 'can you recommend advanced courses in data science'
- 'what courses are trending in web development'
|
| greet-hi | |
| greet-good_bye | - 'sortir'
- 'A plus tard'
- 'See you later'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8333 |
## 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("HussienAhmad/SFT_GradProject")
# Run inference
preds = model("who are you")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 6.2 | 11 |
| Label | Training Sample Count |
|:--------------------|:----------------------|
| greet-hi | 5 |
| greet-who_are_you | 7 |
| greet-good_bye | 5 |
| general-questions | 28 |
| recommendations | 27 |
| website-information | 28 |
### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (4, 4)
- 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 |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0005 | 1 | 0.3442 | - |
| 0.0053 | 10 | 0.2974 | - |
| 0.0105 | 20 | 0.1983 | - |
| 0.0158 | 30 | 0.0645 | - |
| 0.0210 | 40 | 0.3592 | - |
| 0.0263 | 50 | 0.0033 | - |
| 0.0316 | 60 | 0.2558 | - |
| 0.0368 | 70 | 0.2319 | - |
| 0.0421 | 80 | 0.3831 | - |
| 0.0473 | 90 | 0.1864 | - |
| 0.0526 | 100 | 0.2244 | - |
| 0.0579 | 110 | 0.2316 | - |
| 0.0631 | 120 | 0.3702 | - |
| 0.0684 | 130 | 0.0582 | - |
| 0.0736 | 140 | 0.1031 | - |
| 0.0789 | 150 | 0.2882 | - |
| 0.0842 | 160 | 0.1125 | - |
| 0.0894 | 170 | 0.1588 | - |
| 0.0947 | 180 | 0.1672 | - |
| 0.0999 | 190 | 0.0974 | - |
| 0.1052 | 200 | 0.1789 | - |
| 0.1105 | 210 | 0.1032 | - |
| 0.1157 | 220 | 0.1344 | - |
| 0.1210 | 230 | 0.0952 | - |
| 0.1262 | 240 | 0.0891 | - |
| 0.1315 | 250 | 0.4312 | - |
| 0.1368 | 260 | 0.0871 | - |
| 0.1420 | 270 | 0.1482 | - |
| 0.1473 | 280 | 0.0645 | - |
| 0.1526 | 290 | 0.1214 | - |
| 0.1578 | 300 | 0.186 | - |
| 0.1631 | 310 | 0.0516 | - |
| 0.1683 | 320 | 0.0761 | - |
| 0.1736 | 330 | 0.0263 | - |
| 0.1789 | 340 | 0.0588 | - |
| 0.1841 | 350 | 0.016 | - |
| 0.1894 | 360 | 0.0264 | - |
| 0.1946 | 370 | 0.0153 | - |
| 0.1999 | 380 | 0.0091 | - |
| 0.2052 | 390 | 0.0347 | - |
| 0.2104 | 400 | 0.0095 | - |
| 0.2157 | 410 | 0.0262 | - |
| 0.2209 | 420 | 0.0182 | - |
| 0.2262 | 430 | 0.1407 | - |
| 0.2315 | 440 | 0.1451 | - |
| 0.2367 | 450 | 0.0045 | - |
| 0.2420 | 460 | 0.0053 | - |
| 0.2472 | 470 | 0.0038 | - |
| 0.2525 | 480 | 0.1549 | - |
| 0.2578 | 490 | 0.0036 | - |
| 0.2630 | 500 | 0.0079 | - |
| 0.2683 | 510 | 0.0065 | - |
| 0.2735 | 520 | 0.005 | - |
| 0.2788 | 530 | 0.0038 | - |
| 0.2841 | 540 | 0.0283 | - |
| 0.2893 | 550 | 0.0114 | - |
| 0.2946 | 560 | 0.0012 | - |
| 0.2998 | 570 | 0.0165 | - |
| 0.3051 | 580 | 0.0009 | - |
| 0.3104 | 590 | 0.038 | - |
| 0.3156 | 600 | 0.0127 | - |
| 0.3209 | 610 | 0.0019 | - |
| 0.3261 | 620 | 0.003 | - |
| 0.3314 | 630 | 0.0013 | - |
| 0.3367 | 640 | 0.0024 | - |
| 0.3419 | 650 | 0.002 | - |
| 0.3472 | 660 | 0.0017 | - |
| 0.3524 | 670 | 0.0074 | - |
| 0.3577 | 680 | 0.0008 | - |
| 0.3630 | 690 | 0.0015 | - |
| 0.3682 | 700 | 0.0018 | - |
| 0.3735 | 710 | 0.0009 | - |
| 0.3787 | 720 | 0.0019 | - |
| 0.3840 | 730 | 0.0032 | - |
| 0.3893 | 740 | 0.001 | - |
| 0.3945 | 750 | 0.0257 | - |
| 0.3998 | 760 | 0.0018 | - |
| 0.4050 | 770 | 0.001 | - |
| 0.4103 | 780 | 0.0006 | - |
| 0.4156 | 790 | 0.0014 | - |
| 0.4208 | 800 | 0.0012 | - |
| 0.4261 | 810 | 0.018 | - |
| 0.4314 | 820 | 0.0013 | - |
| 0.4366 | 830 | 0.0019 | - |
| 0.4419 | 840 | 0.0006 | - |
| 0.4471 | 850 | 0.0012 | - |
| 0.4524 | 860 | 0.0011 | - |
| 0.4577 | 870 | 0.001 | - |
| 0.4629 | 880 | 0.0017 | - |
| 0.4682 | 890 | 0.002 | - |
| 0.4734 | 900 | 0.0009 | - |
| 0.4787 | 910 | 0.0026 | - |
| 0.4840 | 920 | 0.0009 | - |
| 0.4892 | 930 | 0.0019 | - |
| 0.4945 | 940 | 0.0018 | - |
| 0.4997 | 950 | 0.001 | - |
| 0.5050 | 960 | 0.0022 | - |
| 0.5103 | 970 | 0.0006 | - |
| 0.5155 | 980 | 0.001 | - |
| 0.5208 | 990 | 0.0004 | - |
| 0.5260 | 1000 | 0.0002 | - |
| 0.5313 | 1010 | 0.001 | - |
| 0.5366 | 1020 | 0.001 | - |
| 0.5418 | 1030 | 0.0019 | - |
| 0.5471 | 1040 | 0.0004 | - |
| 0.5523 | 1050 | 0.1705 | - |
| 0.5576 | 1060 | 0.0006 | - |
| 0.5629 | 1070 | 0.0006 | - |
| 0.5681 | 1080 | 0.0007 | - |
| 0.5734 | 1090 | 0.1562 | - |
| 0.5786 | 1100 | 0.0008 | - |
| 0.5839 | 1110 | 0.0016 | - |
| 0.5892 | 1120 | 0.001 | - |
| 0.5944 | 1130 | 0.0003 | - |
| 0.5997 | 1140 | 0.0077 | - |
| 0.6049 | 1150 | 0.0006 | - |
| 0.6102 | 1160 | 0.0008 | - |
| 0.6155 | 1170 | 0.0006 | - |
| 0.6207 | 1180 | 0.0007 | - |
| 0.6260 | 1190 | 0.1438 | - |
| 0.6312 | 1200 | 0.0008 | - |
| 0.6365 | 1210 | 0.0012 | - |
| 0.6418 | 1220 | 0.0005 | - |
| 0.6470 | 1230 | 0.0017 | - |
| 0.6523 | 1240 | 0.0007 | - |
| 0.6575 | 1250 | 0.0004 | - |
| 0.6628 | 1260 | 0.0066 | - |
| 0.6681 | 1270 | 0.0004 | - |
| 0.6733 | 1280 | 0.0002 | - |
| 0.6786 | 1290 | 0.1272 | - |
| 0.6839 | 1300 | 0.0019 | - |
| 0.6891 | 1310 | 0.0014 | - |
| 0.6944 | 1320 | 0.0003 | - |
| 0.6996 | 1330 | 0.0007 | - |
| 0.7049 | 1340 | 0.0003 | - |
| 0.7102 | 1350 | 0.0008 | - |
| 0.7154 | 1360 | 0.0005 | - |
| 0.7207 | 1370 | 0.126 | - |
| 0.7259 | 1380 | 0.0003 | - |
| 0.7312 | 1390 | 0.0013 | - |
| 0.7365 | 1400 | 0.0005 | - |
| 0.7417 | 1410 | 0.0003 | - |
| 0.7470 | 1420 | 0.0003 | - |
| 0.7522 | 1430 | 0.0003 | - |
| 0.7575 | 1440 | 0.0005 | - |
| 0.7628 | 1450 | 0.0009 | - |
| 0.7680 | 1460 | 0.0008 | - |
| 0.7733 | 1470 | 0.0002 | - |
| 0.7785 | 1480 | 0.0003 | - |
| 0.7838 | 1490 | 0.0007 | - |
| 0.7891 | 1500 | 0.0064 | - |
| 0.7943 | 1510 | 0.0004 | - |
| 0.7996 | 1520 | 0.0006 | - |
| 0.8048 | 1530 | 0.0003 | - |
| 0.8101 | 1540 | 0.0005 | - |
| 0.8154 | 1550 | 0.0006 | - |
| 0.8206 | 1560 | 0.0005 | - |
| 0.8259 | 1570 | 0.0004 | - |
| 0.8311 | 1580 | 0.0007 | - |
| 0.8364 | 1590 | 0.0006 | - |
| 0.8417 | 1600 | 0.0002 | - |
| 0.8469 | 1610 | 0.0007 | - |
| 0.8522 | 1620 | 0.0002 | - |
| 0.8574 | 1630 | 0.0005 | - |
| 0.8627 | 1640 | 0.0035 | - |
| 0.8680 | 1650 | 0.0004 | - |
| 0.8732 | 1660 | 0.0025 | - |
| 0.8785 | 1670 | 0.0005 | - |
| 0.8837 | 1680 | 0.0021 | - |
| 0.8890 | 1690 | 0.0003 | - |
| 0.8943 | 1700 | 0.0018 | - |
| 0.8995 | 1710 | 0.0004 | - |
| 0.9048 | 1720 | 0.0002 | - |
| 0.9100 | 1730 | 0.0003 | - |
| 0.9153 | 1740 | 0.0006 | - |
| 0.9206 | 1750 | 0.0002 | - |
| 0.9258 | 1760 | 0.0003 | - |
| 0.9311 | 1770 | 0.0004 | - |
| 0.9363 | 1780 | 0.0004 | - |
| 0.9416 | 1790 | 0.0004 | - |
| 0.9469 | 1800 | 0.0006 | - |
| 0.9521 | 1810 | 0.0007 | - |
| 0.9574 | 1820 | 0.001 | - |
| 0.9627 | 1830 | 0.0003 | - |
| 0.9679 | 1840 | 0.0009 | - |
| 0.9732 | 1850 | 0.0001 | - |
| 0.9784 | 1860 | 0.0006 | - |
| 0.9837 | 1870 | 0.0002 | - |
| 0.9890 | 1880 | 0.0003 | - |
| 0.9942 | 1890 | 0.0004 | - |
| 0.9995 | 1900 | 0.0009 | - |
| 1.0 | 1901 | - | 0.0347 |
| 1.0047 | 1910 | 0.0004 | - |
| 1.0100 | 1920 | 0.0004 | - |
| 1.0153 | 1930 | 0.0005 | - |
| 1.0205 | 1940 | 0.0007 | - |
| 1.0258 | 1950 | 0.0085 | - |
| 1.0310 | 1960 | 0.0003 | - |
| 1.0363 | 1970 | 0.0003 | - |
| 1.0416 | 1980 | 0.0002 | - |
| 1.0468 | 1990 | 0.0009 | - |
| 1.0521 | 2000 | 0.0002 | - |
| 1.0573 | 2010 | 0.0059 | - |
| 1.0626 | 2020 | 0.0007 | - |
| 1.0679 | 2030 | 0.0008 | - |
| 1.0731 | 2040 | 0.0002 | - |
| 1.0784 | 2050 | 0.0002 | - |
| 1.0836 | 2060 | 0.0003 | - |
| 1.0889 | 2070 | 0.0003 | - |
| 1.0942 | 2080 | 0.0002 | - |
| 1.0994 | 2090 | 0.0003 | - |
| 1.1047 | 2100 | 0.0002 | - |
| 1.1099 | 2110 | 0.0065 | - |
| 1.1152 | 2120 | 0.0006 | - |
| 1.1205 | 2130 | 0.0004 | - |
| 1.1257 | 2140 | 0.0035 | - |
| 1.1310 | 2150 | 0.0003 | - |
| 1.1362 | 2160 | 0.0002 | - |
| 1.1415 | 2170 | 0.0002 | - |
| 1.1468 | 2180 | 0.0002 | - |
| 1.1520 | 2190 | 0.001 | - |
| 1.1573 | 2200 | 0.0003 | - |
| 1.1625 | 2210 | 0.0002 | - |
| 1.1678 | 2220 | 0.0002 | - |
| 1.1731 | 2230 | 0.0002 | - |
| 1.1783 | 2240 | 0.0003 | - |
| 1.1836 | 2250 | 0.0002 | - |
| 1.1888 | 2260 | 0.0008 | - |
| 1.1941 | 2270 | 0.0002 | - |
| 1.1994 | 2280 | 0.0018 | - |
| 1.2046 | 2290 | 0.0001 | - |
| 1.2099 | 2300 | 0.0002 | - |
| 1.2151 | 2310 | 0.0005 | - |
| 1.2204 | 2320 | 0.0008 | - |
| 1.2257 | 2330 | 0.0002 | - |
| 1.2309 | 2340 | 0.0003 | - |
| 1.2362 | 2350 | 0.0002 | - |
| 1.2415 | 2360 | 0.0003 | - |
| 1.2467 | 2370 | 0.0001 | - |
| 1.2520 | 2380 | 0.0002 | - |
| 1.2572 | 2390 | 0.0002 | - |
| 1.2625 | 2400 | 0.0002 | - |
| 1.2678 | 2410 | 0.0003 | - |
| 1.2730 | 2420 | 0.0002 | - |
| 1.2783 | 2430 | 0.0002 | - |
| 1.2835 | 2440 | 0.0002 | - |
| 1.2888 | 2450 | 0.0003 | - |
| 1.2941 | 2460 | 0.0004 | - |
| 1.2993 | 2470 | 0.0002 | - |
| 1.3046 | 2480 | 0.0002 | - |
| 1.3098 | 2490 | 0.0006 | - |
| 1.3151 | 2500 | 0.0002 | - |
| 1.3204 | 2510 | 0.0002 | - |
| 1.3256 | 2520 | 0.0001 | - |
| 1.3309 | 2530 | 0.0037 | - |
| 1.3361 | 2540 | 0.0004 | - |
| 1.3414 | 2550 | 0.0003 | - |
| 1.3467 | 2560 | 0.0001 | - |
| 1.3519 | 2570 | 0.0001 | - |
| 1.3572 | 2580 | 0.0003 | - |
| 1.3624 | 2590 | 0.0002 | - |
| 1.3677 | 2600 | 0.0003 | - |
| 1.3730 | 2610 | 0.0003 | - |
| 1.3782 | 2620 | 0.0003 | - |
| 1.3835 | 2630 | 0.0003 | - |
| 1.3887 | 2640 | 0.0002 | - |
| 1.3940 | 2650 | 0.0034 | - |
| 1.3993 | 2660 | 0.0002 | - |
| 1.4045 | 2670 | 0.0004 | - |
| 1.4098 | 2680 | 0.0004 | - |
| 1.4150 | 2690 | 0.0003 | - |
| 1.4203 | 2700 | 0.0003 | - |
| 1.4256 | 2710 | 0.0007 | - |
| 1.4308 | 2720 | 0.0002 | - |
| 1.4361 | 2730 | 0.0004 | - |
| 1.4413 | 2740 | 0.0004 | - |
| 1.4466 | 2750 | 0.0005 | - |
| 1.4519 | 2760 | 0.0003 | - |
| 1.4571 | 2770 | 0.0003 | - |
| 1.4624 | 2780 | 0.0005 | - |
| 1.4676 | 2790 | 0.0015 | - |
| 1.4729 | 2800 | 0.0005 | - |
| 1.4782 | 2810 | 0.0003 | - |
| 1.4834 | 2820 | 0.0003 | - |
| 1.4887 | 2830 | 0.0002 | - |
| 1.4940 | 2840 | 0.0003 | - |
| 1.4992 | 2850 | 0.0004 | - |
| 1.5045 | 2860 | 0.0025 | - |
| 1.5097 | 2870 | 0.0001 | - |
| 1.5150 | 2880 | 0.0002 | - |
| 1.5203 | 2890 | 0.0004 | - |
| 1.5255 | 2900 | 0.0001 | - |
| 1.5308 | 2910 | 0.0003 | - |
| 1.5360 | 2920 | 0.0006 | - |
| 1.5413 | 2930 | 0.0001 | - |
| 1.5466 | 2940 | 0.0001 | - |
| 1.5518 | 2950 | 0.0004 | - |
| 1.5571 | 2960 | 0.0002 | - |
| 1.5623 | 2970 | 0.0006 | - |
| 1.5676 | 2980 | 0.0003 | - |
| 1.5729 | 2990 | 0.001 | - |
| 1.5781 | 3000 | 0.0003 | - |
| 1.5834 | 3010 | 0.0002 | - |
| 1.5886 | 3020 | 0.0003 | - |
| 1.5939 | 3030 | 0.0002 | - |
| 1.5992 | 3040 | 0.0001 | - |
| 1.6044 | 3050 | 0.0002 | - |
| 1.6097 | 3060 | 0.0002 | - |
| 1.6149 | 3070 | 0.0002 | - |
| 1.6202 | 3080 | 0.0001 | - |
| 1.6255 | 3090 | 0.0002 | - |
| 1.6307 | 3100 | 0.0002 | - |
| 1.6360 | 3110 | 0.0001 | - |
| 1.6412 | 3120 | 0.0001 | - |
| 1.6465 | 3130 | 0.0002 | - |
| 1.6518 | 3140 | 0.0003 | - |
| 1.6570 | 3150 | 0.0002 | - |
| 1.6623 | 3160 | 0.0002 | - |
| 1.6675 | 3170 | 0.0001 | - |
| 1.6728 | 3180 | 0.0002 | - |
| 1.6781 | 3190 | 0.0002 | - |
| 1.6833 | 3200 | 0.0008 | - |
| 1.6886 | 3210 | 0.0002 | - |
| 1.6938 | 3220 | 0.0003 | - |
| 1.6991 | 3230 | 0.0001 | - |
| 1.7044 | 3240 | 0.0001 | - |
| 1.7096 | 3250 | 0.0001 | - |
| 1.7149 | 3260 | 0.0002 | - |
| 1.7201 | 3270 | 0.0003 | - |
| 1.7254 | 3280 | 0.0001 | - |
| 1.7307 | 3290 | 0.0003 | - |
| 1.7359 | 3300 | 0.0001 | - |
| 1.7412 | 3310 | 0.0003 | - |
| 1.7464 | 3320 | 0.0002 | - |
| 1.7517 | 3330 | 0.0002 | - |
| 1.7570 | 3340 | 0.0001 | - |
| 1.7622 | 3350 | 0.0002 | - |
| 1.7675 | 3360 | 0.0001 | - |
| 1.7728 | 3370 | 0.0005 | - |
| 1.7780 | 3380 | 0.0001 | - |
| 1.7833 | 3390 | 0.0003 | - |
| 1.7885 | 3400 | 0.0002 | - |
| 1.7938 | 3410 | 0.0003 | - |
| 1.7991 | 3420 | 0.0002 | - |
| 1.8043 | 3430 | 0.0002 | - |
| 1.8096 | 3440 | 0.0009 | - |
| 1.8148 | 3450 | 0.0001 | - |
| 1.8201 | 3460 | 0.0005 | - |
| 1.8254 | 3470 | 0.0002 | - |
| 1.8306 | 3480 | 0.0004 | - |
| 1.8359 | 3490 | 0.0002 | - |
| 1.8411 | 3500 | 0.0001 | - |
| 1.8464 | 3510 | 0.0001 | - |
| 1.8517 | 3520 | 0.0003 | - |
| 1.8569 | 3530 | 0.0001 | - |
| 1.8622 | 3540 | 0.0002 | - |
| 1.8674 | 3550 | 0.0002 | - |
| 1.8727 | 3560 | 0.0011 | - |
| 1.8780 | 3570 | 0.0003 | - |
| 1.8832 | 3580 | 0.0003 | - |
| 1.8885 | 3590 | 0.0002 | - |
| 1.8937 | 3600 | 0.0001 | - |
| 1.8990 | 3610 | 0.0001 | - |
| 1.9043 | 3620 | 0.0002 | - |
| 1.9095 | 3630 | 0.0001 | - |
| 1.9148 | 3640 | 0.0002 | - |
| 1.9200 | 3650 | 0.0002 | - |
| 1.9253 | 3660 | 0.0002 | - |
| 1.9306 | 3670 | 0.0002 | - |
| 1.9358 | 3680 | 0.0001 | - |
| 1.9411 | 3690 | 0.0002 | - |
| 1.9463 | 3700 | 0.0003 | - |
| 1.9516 | 3710 | 0.0006 | - |
| 1.9569 | 3720 | 0.0004 | - |
| 1.9621 | 3730 | 0.0001 | - |
| 1.9674 | 3740 | 0.0002 | - |
| 1.9726 | 3750 | 0.0004 | - |
| 1.9779 | 3760 | 0.0002 | - |
| 1.9832 | 3770 | 0.0004 | - |
| 1.9884 | 3780 | 0.0003 | - |
| 1.9937 | 3790 | 0.0002 | - |
| 1.9989 | 3800 | 0.0002 | - |
| 2.0 | 3802 | - | 0.0333 |
| 2.0042 | 3810 | 0.0001 | - |
| 2.0095 | 3820 | 0.0002 | - |
| 2.0147 | 3830 | 0.0004 | - |
| 2.0200 | 3840 | 0.0005 | - |
| 2.0252 | 3850 | 0.0002 | - |
| 2.0305 | 3860 | 0.0001 | - |
| 2.0358 | 3870 | 0.0005 | - |
| 2.0410 | 3880 | 0.0002 | - |
| 2.0463 | 3890 | 0.0002 | - |
| 2.0516 | 3900 | 0.0002 | - |
| 2.0568 | 3910 | 0.0003 | - |
| 2.0621 | 3920 | 0.0002 | - |
| 2.0673 | 3930 | 0.0005 | - |
| 2.0726 | 3940 | 0.0002 | - |
| 2.0779 | 3950 | 0.0001 | - |
| 2.0831 | 3960 | 0.0001 | - |
| 2.0884 | 3970 | 0.0003 | - |
| 2.0936 | 3980 | 0.0001 | - |
| 2.0989 | 3990 | 0.0002 | - |
| 2.1042 | 4000 | 0.0001 | - |
| 2.1094 | 4010 | 0.0001 | - |
| 2.1147 | 4020 | 0.0001 | - |
| 2.1199 | 4030 | 0.0004 | - |
| 2.1252 | 4040 | 0.0002 | - |
| 2.1305 | 4050 | 0.0003 | - |
| 2.1357 | 4060 | 0.0002 | - |
| 2.1410 | 4070 | 0.0001 | - |
| 2.1462 | 4080 | 0.0001 | - |
| 2.1515 | 4090 | 0.0001 | - |
| 2.1568 | 4100 | 0.0001 | - |
| 2.1620 | 4110 | 0.0001 | - |
| 2.1673 | 4120 | 0.0001 | - |
| 2.1725 | 4130 | 0.0001 | - |
| 2.1778 | 4140 | 0.0001 | - |
| 2.1831 | 4150 | 0.0009 | - |
| 2.1883 | 4160 | 0.0001 | - |
| 2.1936 | 4170 | 0.0003 | - |
| 2.1988 | 4180 | 0.0001 | - |
| 2.2041 | 4190 | 0.0002 | - |
| 2.2094 | 4200 | 0.0003 | - |
| 2.2146 | 4210 | 0.0008 | - |
| 2.2199 | 4220 | 0.0002 | - |
| 2.2251 | 4230 | 0.0004 | - |
| 2.2304 | 4240 | 0.0002 | - |
| 2.2357 | 4250 | 0.0001 | - |
| 2.2409 | 4260 | 0.0004 | - |
| 2.2462 | 4270 | 0.0001 | - |
| 2.2514 | 4280 | 0.0001 | - |
| 2.2567 | 4290 | 0.0001 | - |
| 2.2620 | 4300 | 0.0001 | - |
| 2.2672 | 4310 | 0.0002 | - |
| 2.2725 | 4320 | 0.0002 | - |
| 2.2777 | 4330 | 0.0002 | - |
| 2.2830 | 4340 | 0.0002 | - |
| 2.2883 | 4350 | 0.0001 | - |
| 2.2935 | 4360 | 0.0001 | - |
| 2.2988 | 4370 | 0.0001 | - |
| 2.3041 | 4380 | 0.0004 | - |
| 2.3093 | 4390 | 0.0002 | - |
| 2.3146 | 4400 | 0.0001 | - |
| 2.3198 | 4410 | 0.0004 | - |
| 2.3251 | 4420 | 0.0001 | - |
| 2.3304 | 4430 | 0.0001 | - |
| 2.3356 | 4440 | 0.0001 | - |
| 2.3409 | 4450 | 0.0001 | - |
| 2.3461 | 4460 | 0.0001 | - |
| 2.3514 | 4470 | 0.0002 | - |
| 2.3567 | 4480 | 0.0004 | - |
| 2.3619 | 4490 | 0.0003 | - |
| 2.3672 | 4500 | 0.0002 | - |
| 2.3724 | 4510 | 0.0001 | - |
| 2.3777 | 4520 | 0.0001 | - |
| 2.3830 | 4530 | 0.0001 | - |
| 2.3882 | 4540 | 0.0001 | - |
| 2.3935 | 4550 | 0.0001 | - |
| 2.3987 | 4560 | 0.0002 | - |
| 2.4040 | 4570 | 0.0001 | - |
| 2.4093 | 4580 | 0.0001 | - |
| 2.4145 | 4590 | 0.0001 | - |
| 2.4198 | 4600 | 0.0001 | - |
| 2.4250 | 4610 | 0.0001 | - |
| 2.4303 | 4620 | 0.0008 | - |
| 2.4356 | 4630 | 0.0001 | - |
| 2.4408 | 4640 | 0.0002 | - |
| 2.4461 | 4650 | 0.0001 | - |
| 2.4513 | 4660 | 0.0001 | - |
| 2.4566 | 4670 | 0.0001 | - |
| 2.4619 | 4680 | 0.0001 | - |
| 2.4671 | 4690 | 0.0001 | - |
| 2.4724 | 4700 | 0.0001 | - |
| 2.4776 | 4710 | 0.0001 | - |
| 2.4829 | 4720 | 0.0001 | - |
| 2.4882 | 4730 | 0.0002 | - |
| 2.4934 | 4740 | 0.0001 | - |
| 2.4987 | 4750 | 0.0001 | - |
| 2.5039 | 4760 | 0.0008 | - |
| 2.5092 | 4770 | 0.0002 | - |
| 2.5145 | 4780 | 0.0001 | - |
| 2.5197 | 4790 | 0.0001 | - |
| 2.5250 | 4800 | 0.0007 | - |
| 2.5302 | 4810 | 0.0003 | - |
| 2.5355 | 4820 | 0.0001 | - |
| 2.5408 | 4830 | 0.0001 | - |
| 2.5460 | 4840 | 0.0001 | - |
| 2.5513 | 4850 | 0.0003 | - |
| 2.5565 | 4860 | 0.0001 | - |
| 2.5618 | 4870 | 0.0001 | - |
| 2.5671 | 4880 | 0.0002 | - |
| 2.5723 | 4890 | 0.0001 | - |
| 2.5776 | 4900 | 0.0001 | - |
| 2.5829 | 4910 | 0.0003 | - |
| 2.5881 | 4920 | 0.0001 | - |
| 2.5934 | 4930 | 0.0002 | - |
| 2.5986 | 4940 | 0.0003 | - |
| 2.6039 | 4950 | 0.0001 | - |
| 2.6092 | 4960 | 0.0002 | - |
| 2.6144 | 4970 | 0.0001 | - |
| 2.6197 | 4980 | 0.0002 | - |
| 2.6249 | 4990 | 0.0002 | - |
| 2.6302 | 5000 | 0.0002 | - |
| 2.6355 | 5010 | 0.0004 | - |
| 2.6407 | 5020 | 0.0001 | - |
| 2.6460 | 5030 | 0.0001 | - |
| 2.6512 | 5040 | 0.0004 | - |
| 2.6565 | 5050 | 0.0001 | - |
| 2.6618 | 5060 | 0.0002 | - |
| 2.6670 | 5070 | 0.0014 | - |
| 2.6723 | 5080 | 0.0003 | - |
| 2.6775 | 5090 | 0.0001 | - |
| 2.6828 | 5100 | 0.0003 | - |
| 2.6881 | 5110 | 0.0001 | - |
| 2.6933 | 5120 | 0.0001 | - |
| 2.6986 | 5130 | 0.0009 | - |
| 2.7038 | 5140 | 0.0002 | - |
| 2.7091 | 5150 | 0.0003 | - |
| 2.7144 | 5160 | 0.0001 | - |
| 2.7196 | 5170 | 0.0001 | - |
| 2.7249 | 5180 | 0.0002 | - |
| 2.7301 | 5190 | 0.0001 | - |
| 2.7354 | 5200 | 0.0001 | - |
| 2.7407 | 5210 | 0.0001 | - |
| 2.7459 | 5220 | 0.0002 | - |
| 2.7512 | 5230 | 0.0004 | - |
| 2.7564 | 5240 | 0.0001 | - |
| 2.7617 | 5250 | 0.0001 | - |
| 2.7670 | 5260 | 0.0004 | - |
| 2.7722 | 5270 | 0.0003 | - |
| 2.7775 | 5280 | 0.0002 | - |
| 2.7827 | 5290 | 0.0002 | - |
| 2.7880 | 5300 | 0.0001 | - |
| 2.7933 | 5310 | 0.0003 | - |
| 2.7985 | 5320 | 0.0001 | - |
| 2.8038 | 5330 | 0.0005 | - |
| 2.8090 | 5340 | 0.0001 | - |
| 2.8143 | 5350 | 0.0001 | - |
| 2.8196 | 5360 | 0.0001 | - |
| 2.8248 | 5370 | 0.0001 | - |
| 2.8301 | 5380 | 0.0003 | - |
| 2.8353 | 5390 | 0.0001 | - |
| 2.8406 | 5400 | 0.0008 | - |
| 2.8459 | 5410 | 0.0001 | - |
| 2.8511 | 5420 | 0.0001 | - |
| 2.8564 | 5430 | 0.0001 | - |
| 2.8617 | 5440 | 0.0002 | - |
| 2.8669 | 5450 | 0.0001 | - |
| 2.8722 | 5460 | 0.0004 | - |
| 2.8774 | 5470 | 0.0001 | - |
| 2.8827 | 5480 | 0.0001 | - |
| 2.8880 | 5490 | 0.0002 | - |
| 2.8932 | 5500 | 0.0001 | - |
| 2.8985 | 5510 | 0.0001 | - |
| 2.9037 | 5520 | 0.0001 | - |
| 2.9090 | 5530 | 0.0002 | - |
| 2.9143 | 5540 | 0.0002 | - |
| 2.9195 | 5550 | 0.0001 | - |
| 2.9248 | 5560 | 0.0001 | - |
| 2.9300 | 5570 | 0.0005 | - |
| 2.9353 | 5580 | 0.0002 | - |
| 2.9406 | 5590 | 0.0001 | - |
| 2.9458 | 5600 | 0.0001 | - |
| 2.9511 | 5610 | 0.0003 | - |
| 2.9563 | 5620 | 0.0001 | - |
| 2.9616 | 5630 | 0.0001 | - |
| 2.9669 | 5640 | 0.0001 | - |
| 2.9721 | 5650 | 0.0006 | - |
| 2.9774 | 5660 | 0.0001 | - |
| 2.9826 | 5670 | 0.0001 | - |
| 2.9879 | 5680 | 0.0001 | - |
| 2.9932 | 5690 | 0.0001 | - |
| 2.9984 | 5700 | 0.0001 | - |
| 3.0 | 5703 | - | 0.0349 |
| 3.0037 | 5710 | 0.0001 | - |
| 3.0089 | 5720 | 0.0001 | - |
| 3.0142 | 5730 | 0.0002 | - |
| 3.0195 | 5740 | 0.0001 | - |
| 3.0247 | 5750 | 0.0002 | - |
| 3.0300 | 5760 | 0.0001 | - |
| 3.0352 | 5770 | 0.0008 | - |
| 3.0405 | 5780 | 0.0004 | - |
| 3.0458 | 5790 | 0.0003 | - |
| 3.0510 | 5800 | 0.0001 | - |
| 3.0563 | 5810 | 0.0001 | - |
| 3.0615 | 5820 | 0.0006 | - |
| 3.0668 | 5830 | 0.0002 | - |
| 3.0721 | 5840 | 0.0001 | - |
| 3.0773 | 5850 | 0.0002 | - |
| 3.0826 | 5860 | 0.0002 | - |
| 3.0878 | 5870 | 0.0002 | - |
| 3.0931 | 5880 | 0.0002 | - |
| 3.0984 | 5890 | 0.0001 | - |
| 3.1036 | 5900 | 0.0001 | - |
| 3.1089 | 5910 | 0.0001 | - |
| 3.1142 | 5920 | 0.0001 | - |
| 3.1194 | 5930 | 0.0001 | - |
| 3.1247 | 5940 | 0.0001 | - |
| 3.1299 | 5950 | 0.0002 | - |
| 3.1352 | 5960 | 0.0003 | - |
| 3.1405 | 5970 | 0.0003 | - |
| 3.1457 | 5980 | 0.0009 | - |
| 3.1510 | 5990 | 0.0001 | - |
| 3.1562 | 6000 | 0.0001 | - |
| 3.1615 | 6010 | 0.0002 | - |
| 3.1668 | 6020 | 0.0001 | - |
| 3.1720 | 6030 | 0.0001 | - |
| 3.1773 | 6040 | 0.0001 | - |
| 3.1825 | 6050 | 0.0002 | - |
| 3.1878 | 6060 | 0.0001 | - |
| 3.1931 | 6070 | 0.0001 | - |
| 3.1983 | 6080 | 0.0002 | - |
| 3.2036 | 6090 | 0.0001 | - |
| 3.2088 | 6100 | 0.0002 | - |
| 3.2141 | 6110 | 0.0003 | - |
| 3.2194 | 6120 | 0.0001 | - |
| 3.2246 | 6130 | 0.0001 | - |
| 3.2299 | 6140 | 0.0001 | - |
| 3.2351 | 6150 | 0.0001 | - |
| 3.2404 | 6160 | 0.0001 | - |
| 3.2457 | 6170 | 0.0001 | - |
| 3.2509 | 6180 | 0.0001 | - |
| 3.2562 | 6190 | 0.0001 | - |
| 3.2614 | 6200 | 0.0001 | - |
| 3.2667 | 6210 | 0.0002 | - |
| 3.2720 | 6220 | 0.0001 | - |
| 3.2772 | 6230 | 0.0001 | - |
| 3.2825 | 6240 | 0.0001 | - |
| 3.2877 | 6250 | 0.0002 | - |
| 3.2930 | 6260 | 0.0001 | - |
| 3.2983 | 6270 | 0.0001 | - |
| 3.3035 | 6280 | 0.0002 | - |
| 3.3088 | 6290 | 0.0001 | - |
| 3.3140 | 6300 | 0.0001 | - |
| 3.3193 | 6310 | 0.0001 | - |
| 3.3246 | 6320 | 0.0001 | - |
| 3.3298 | 6330 | 0.0 | - |
| 3.3351 | 6340 | 0.0003 | - |
| 3.3403 | 6350 | 0.0002 | - |
| 3.3456 | 6360 | 0.0001 | - |
| 3.3509 | 6370 | 0.0001 | - |
| 3.3561 | 6380 | 0.0003 | - |
| 3.3614 | 6390 | 0.0 | - |
| 3.3666 | 6400 | 0.0001 | - |
| 3.3719 | 6410 | 0.0001 | - |
| 3.3772 | 6420 | 0.0001 | - |
| 3.3824 | 6430 | 0.0001 | - |
| 3.3877 | 6440 | 0.0001 | - |
| 3.3930 | 6450 | 0.0003 | - |
| 3.3982 | 6460 | 0.0002 | - |
| 3.4035 | 6470 | 0.0001 | - |
| 3.4087 | 6480 | 0.0002 | - |
| 3.4140 | 6490 | 0.0003 | - |
| 3.4193 | 6500 | 0.0 | - |
| 3.4245 | 6510 | 0.0001 | - |
| 3.4298 | 6520 | 0.0002 | - |
| 3.4350 | 6530 | 0.0001 | - |
| 3.4403 | 6540 | 0.0001 | - |
| 3.4456 | 6550 | 0.0001 | - |
| 3.4508 | 6560 | 0.0001 | - |
| 3.4561 | 6570 | 0.0001 | - |
| 3.4613 | 6580 | 0.0001 | - |
| 3.4666 | 6590 | 0.0001 | - |
| 3.4719 | 6600 | 0.0001 | - |
| 3.4771 | 6610 | 0.0001 | - |
| 3.4824 | 6620 | 0.0003 | - |
| 3.4876 | 6630 | 0.0001 | - |
| 3.4929 | 6640 | 0.0001 | - |
| 3.4982 | 6650 | 0.0001 | - |
| 3.5034 | 6660 | 0.0002 | - |
| 3.5087 | 6670 | 0.0001 | - |
| 3.5139 | 6680 | 0.0007 | - |
| 3.5192 | 6690 | 0.0004 | - |
| 3.5245 | 6700 | 0.0001 | - |
| 3.5297 | 6710 | 0.0001 | - |
| 3.5350 | 6720 | 0.0001 | - |
| 3.5402 | 6730 | 0.0001 | - |
| 3.5455 | 6740 | 0.0001 | - |
| 3.5508 | 6750 | 0.0001 | - |
| 3.5560 | 6760 | 0.0001 | - |
| 3.5613 | 6770 | 0.0001 | - |
| 3.5665 | 6780 | 0.0001 | - |
| 3.5718 | 6790 | 0.0001 | - |
| 3.5771 | 6800 | 0.0 | - |
| 3.5823 | 6810 | 0.0001 | - |
| 3.5876 | 6820 | 0.0001 | - |
| 3.5928 | 6830 | 0.0004 | - |
| 3.5981 | 6840 | 0.0001 | - |
| 3.6034 | 6850 | 0.0001 | - |
| 3.6086 | 6860 | 0.0001 | - |
| 3.6139 | 6870 | 0.0 | - |
| 3.6191 | 6880 | 0.0001 | - |
| 3.6244 | 6890 | 0.0001 | - |
| 3.6297 | 6900 | 0.0001 | - |
| 3.6349 | 6910 | 0.0001 | - |
| 3.6402 | 6920 | 0.0002 | - |
| 3.6454 | 6930 | 0.0001 | - |
| 3.6507 | 6940 | 0.0001 | - |
| 3.6560 | 6950 | 0.0 | - |
| 3.6612 | 6960 | 0.0 | - |
| 3.6665 | 6970 | 0.0001 | - |
| 3.6718 | 6980 | 0.0001 | - |
| 3.6770 | 6990 | 0.0002 | - |
| 3.6823 | 7000 | 0.0001 | - |
| 3.6875 | 7010 | 0.0001 | - |
| 3.6928 | 7020 | 0.0001 | - |
| 3.6981 | 7030 | 0.0001 | - |
| 3.7033 | 7040 | 0.0001 | - |
| 3.7086 | 7050 | 0.0002 | - |
| 3.7138 | 7060 | 0.0001 | - |
| 3.7191 | 7070 | 0.0001 | - |
| 3.7244 | 7080 | 0.0001 | - |
| 3.7296 | 7090 | 0.0001 | - |
| 3.7349 | 7100 | 0.0001 | - |
| 3.7401 | 7110 | 0.0001 | - |
| 3.7454 | 7120 | 0.0001 | - |
| 3.7507 | 7130 | 0.0003 | - |
| 3.7559 | 7140 | 0.0001 | - |
| 3.7612 | 7150 | 0.0001 | - |
| 3.7664 | 7160 | 0.0002 | - |
| 3.7717 | 7170 | 0.0002 | - |
| 3.7770 | 7180 | 0.0001 | - |
| 3.7822 | 7190 | 0.0001 | - |
| 3.7875 | 7200 | 0.0001 | - |
| 3.7927 | 7210 | 0.0003 | - |
| 3.7980 | 7220 | 0.0001 | - |
| 3.8033 | 7230 | 0.0001 | - |
| 3.8085 | 7240 | 0.0001 | - |
| 3.8138 | 7250 | 0.0001 | - |
| 3.8190 | 7260 | 0.0001 | - |
| 3.8243 | 7270 | 0.0002 | - |
| 3.8296 | 7280 | 0.0002 | - |
| 3.8348 | 7290 | 0.0001 | - |
| 3.8401 | 7300 | 0.0001 | - |
| 3.8453 | 7310 | 0.0001 | - |
| 3.8506 | 7320 | 0.0001 | - |
| 3.8559 | 7330 | 0.0001 | - |
| 3.8611 | 7340 | 0.0006 | - |
| 3.8664 | 7350 | 0.0001 | - |
| 3.8716 | 7360 | 0.0001 | - |
| 3.8769 | 7370 | 0.0 | - |
| 3.8822 | 7380 | 0.0003 | - |
| 3.8874 | 7390 | 0.0001 | - |
| 3.8927 | 7400 | 0.0001 | - |
| 3.8979 | 7410 | 0.0001 | - |
| 3.9032 | 7420 | 0.0001 | - |
| 3.9085 | 7430 | 0.0002 | - |
| 3.9137 | 7440 | 0.0001 | - |
| 3.9190 | 7450 | 0.0002 | - |
| 3.9243 | 7460 | 0.0001 | - |
| 3.9295 | 7470 | 0.0001 | - |
| 3.9348 | 7480 | 0.0002 | - |
| 3.9400 | 7490 | 0.0001 | - |
| 3.9453 | 7500 | 0.0002 | - |
| 3.9506 | 7510 | 0.0001 | - |
| 3.9558 | 7520 | 0.0001 | - |
| 3.9611 | 7530 | 0.0001 | - |
| 3.9663 | 7540 | 0.0001 | - |
| 3.9716 | 7550 | 0.0001 | - |
| 3.9769 | 7560 | 0.0002 | - |
| 3.9821 | 7570 | 0.0001 | - |
| 3.9874 | 7580 | 0.0001 | - |
| 3.9926 | 7590 | 0.0001 | - |
| 3.9979 | 7600 | 0.0001 | - |
| **4.0** | **7604** | **-** | **0.0319** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.37.0
- PyTorch: 2.5.1+cu121
- Datasets: 3.1.0
- 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}
}
```