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Davidozito/
fewshot-2500-samples

Name: fewshot-2500-samples
Availability: InStock

generated_from_trainer

autotrain_compatible

endpoints_compatible

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fewshot-2500-samples

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4709
Precision: 0.5379
Recall: 0.4151
F1 Macro: 0.4036
Accuracy: 0.58
Classification Report: precision recall f1-score support

None 1.00 0.27 0.42 63

Minimal 0.41 0.71 0.52 52 Basic 0.64 0.86 0.73 95 Good 0.64 0.23 0.34 39 Excellent 0.00 0.00 0.00 1

accuracy 0.58 250 macro avg 0.54 0.42 0.40 250 weighted avg 0.68 0.58 0.55 250
Mse: 0.4709

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1 Macro	Accuracy	Classification Report	Mse
No log	0	0	0.4752	0.5583	0.4159	0.3894	0.588	precision recall f1-score support

    None       1.00      0.27      0.42        63
 Minimal       0.43      0.81      0.56        52
   Basic       0.65      0.87      0.74        95
    Good       0.71      0.13      0.22        39

Excellent 0.00 0.00 0.00 1

accuracy 0.59 250

macro avg 0.56 0.42 0.39 250 weighted avg 0.70 0.59 0.54 250 | 0.4752 | | 0.5596 | 0.2482 | 70 | 0.4776 | 0.5583 | 0.4159 | 0.3894 | 0.588 | precision recall f1-score support

    None       1.00      0.27      0.42        63
 Minimal       0.43      0.81      0.56        52
   Basic       0.65      0.87      0.74        95
    Good       0.71      0.13      0.22        39

Excellent 0.00 0.00 0.00 1

accuracy 0.59 250

macro avg 0.56 0.42 0.39 250 weighted avg 0.70 0.59 0.54 250 | 0.4776 | | 0.642 | 0.4965 | 140 | 0.5114 | 0.5188 | 0.3953 | 0.3826 | 0.552 | precision recall f1-score support

    None       1.00      0.19      0.32        63
 Minimal       0.37      0.62      0.46        52
   Basic       0.63      0.86      0.73        95
    Good       0.60      0.31      0.41        39

Excellent 0.00 0.00 0.00 1

accuracy 0.55 250

macro avg 0.52 0.40 0.38 250 weighted avg 0.66 0.55 0.52 250 | 0.5114 | | 0.6216 | 0.7447 | 210 | 0.4966 | 0.5272 | 0.3981 | 0.3839 | 0.56 | precision recall f1-score support

    None       1.00      0.21      0.34        63
 Minimal       0.38      0.65      0.48        52
   Basic       0.63      0.87      0.73        95
    Good       0.62      0.26      0.36        39

Excellent 0.00 0.00 0.00 1

accuracy 0.56 250

macro avg 0.53 0.40 0.38 250 weighted avg 0.67 0.56 0.52 250 | 0.4966 | | 0.6004 | 0.9929 | 280 | 0.4998 | 0.5085 | 0.3960 | 0.3837 | 0.548 | precision recall f1-score support

    None       1.00      0.21      0.34        63
 Minimal       0.38      0.63      0.47        52
   Basic       0.62      0.83      0.71        95
    Good       0.55      0.31      0.39        39

Excellent 0.00 0.00 0.00 1

accuracy 0.55 250

macro avg 0.51 0.40 0.38 250 weighted avg 0.65 0.55 0.52 250 | 0.4998 | | 0.6307 | 1.2411 | 350 | 0.5296 | 0.4997 | 0.3857 | 0.3718 | 0.528 | precision recall f1-score support

    None       1.00      0.16      0.27        63
 Minimal       0.34      0.54      0.41        52
   Basic       0.61      0.82      0.70        95
    Good       0.55      0.41      0.47        39

Excellent 0.00 0.00 0.00 1

accuracy 0.53 250

macro avg 0.50 0.39 0.37 250 weighted avg 0.64 0.53 0.49 250 | 0.5296 | | 0.5859 | 1.4894 | 420 | 0.5058 | 0.5153 | 0.4044 | 0.3920 | 0.552 | precision recall f1-score support

    None       1.00      0.19      0.32        63
 Minimal       0.37      0.62      0.46        52
   Basic       0.63      0.83      0.72        95
    Good       0.58      0.38      0.46        39

Excellent 0.00 0.00 0.00 1

accuracy 0.55 250

macro avg 0.52 0.40 0.39 250 weighted avg 0.66 0.55 0.52 250 | 0.5058 | | 0.564 | 1.7376 | 490 | 0.4709 | 0.5379 | 0.4151 | 0.4036 | 0.58 | precision recall f1-score support

    None       1.00      0.27      0.42        63
 Minimal       0.41      0.71      0.52        52
   Basic       0.64      0.86      0.73        95
    Good       0.64      0.23      0.34        39

Excellent 0.00 0.00 0.00 1

accuracy 0.58 250

macro avg 0.54 0.42 0.40 250 weighted avg 0.68 0.58 0.55 250 | 0.4709 | | 0.6084 | 1.9858 | 560 | 0.4877 | 0.5181 | 0.4125 | 0.4031 | 0.56 | precision recall f1-score support

    None       1.00      0.22      0.36        63
 Minimal       0.38      0.63      0.47        52
   Basic       0.63      0.82      0.72        95
    Good       0.58      0.38      0.46        39

Excellent 0.00 0.00 0.00 1

accuracy 0.56 250

macro avg 0.52 0.41 0.40 250 weighted avg 0.66 0.56 0.53 250 | 0.4877 | | 0.5792 | 2.2340 | 630 | 0.4811 | 0.5248 | 0.4265 | 0.4185 | 0.576 | precision recall f1-score support

    None       1.00      0.25      0.41        63
 Minimal       0.40      0.67      0.50        52
   Basic       0.64      0.82      0.72        95
    Good       0.58      0.38      0.46        39

Excellent 0.00 0.00 0.00 1

accuracy 0.58 250

macro avg 0.52 0.43 0.42 250 weighted avg 0.67 0.58 0.55 250 | 0.4811 | | 0.584 | 2.4823 | 700 | 0.4827 | 0.5248 | 0.4265 | 0.4185 | 0.576 | precision recall f1-score support

    None       1.00      0.25      0.41        63
 Minimal       0.40      0.67      0.50        52
   Basic       0.64      0.82      0.72        95
    Good       0.58      0.38      0.46        39

Excellent 0.00 0.00 0.00 1

accuracy 0.58 250

macro avg 0.52 0.43 0.42 250 weighted avg 0.67 0.58 0.55 250 | 0.4827 | | 0.5456 | 2.7305 | 770 | 0.4760 | 0.5248 | 0.4265 | 0.4185 | 0.576 | precision recall f1-score support

    None       1.00      0.25      0.41        63
 Minimal       0.40      0.67      0.50        52
   Basic       0.64      0.82      0.72        95
    Good       0.58      0.38      0.46        39

Excellent 0.00 0.00 0.00 1

accuracy 0.58 250

macro avg 0.52 0.43 0.42 250 weighted avg 0.67 0.58 0.55 250 | 0.4760 | | 0.6446 | 2.9787 | 840 | 0.4901 | 0.5155 | 0.4106 | 0.3998 | 0.556 | precision recall f1-score support

    None       1.00      0.21      0.34        63
 Minimal       0.37      0.62      0.46        52
   Basic       0.63      0.82      0.72        95
    Good       0.57      0.41      0.48        39

Excellent 0.00 0.00 0.00 1

accuracy 0.56 250

macro avg 0.52 0.41 0.40 250 weighted avg 0.66 0.56 0.53 250 | 0.4901 | | 0.5425 | 3.2270 | 910 | 0.4976 | 0.5108 | 0.4085 | 0.3973 | 0.552 | precision recall f1-score support

    None       1.00      0.21      0.34        63
 Minimal       0.38      0.62      0.47        52
   Basic       0.63      0.81      0.71        95
    Good       0.55      0.41      0.47        39

Excellent 0.00 0.00 0.00 1

accuracy 0.55 250

macro avg 0.51 0.41 0.40 250 weighted avg 0.65 0.55 0.53 250 | 0.4976 | | 0.5965 | 3.4752 | 980 | 0.4935 | 0.5110 | 0.4085 | 0.3972 | 0.552 | precision recall f1-score support

    None       1.00      0.21      0.34        63
 Minimal       0.37      0.62      0.46        52
   Basic       0.63      0.81      0.71        95
    Good       0.55      0.41      0.47        39

Excellent 0.00 0.00 0.00 1

accuracy 0.55 250

macro avg 0.51 0.41 0.40 250 weighted avg 0.66 0.55 0.53 250 | 0.4935 | | 0.6324 | 3.7234 | 1050 | 0.4992 | 0.5083 | 0.4047 | 0.3944 | 0.548 | precision recall f1-score support

    None       1.00      0.21      0.34        63
 Minimal       0.37      0.60      0.46        52
   Basic       0.62      0.81      0.70        95
    Good       0.55      0.41      0.47        39

Excellent 0.00 0.00 0.00 1

accuracy 0.55 250

macro avg 0.51 0.40 0.39 250 weighted avg 0.65 0.55 0.52 250 | 0.4992 | | 0.5369 | 3.9716 | 1120 | 0.4784 | 0.5265 | 0.4278 | 0.4210 | 0.576 | precision recall f1-score support

    None       1.00      0.25      0.41        63
 Minimal       0.40      0.65      0.49        52
   Basic       0.64      0.82      0.72        95
    Good       0.59      0.41      0.48        39

Excellent 0.00 0.00 0.00 1

accuracy 0.58 250

macro avg 0.53 0.43 0.42 250 weighted avg 0.67 0.58 0.55 250 | 0.4784 | | 0.6037 | 4.2199 | 1190 | 0.4771 | 0.5265 | 0.4278 | 0.4210 | 0.576 | precision recall f1-score support

    None       1.00      0.25      0.41        63
 Minimal       0.40      0.65      0.49        52
   Basic       0.64      0.82      0.72        95
    Good       0.59      0.41      0.48        39

Excellent 0.00 0.00 0.00 1

accuracy 0.58 250

macro avg 0.53 0.43 0.42 250 weighted avg 0.67 0.58 0.55 250 | 0.4771 | | 0.529 | 4.4681 | 1260 | 0.4785 | 0.5240 | 0.4240 | 0.4181 | 0.572 | precision recall f1-score support

    None       1.00      0.25      0.41        63
 Minimal       0.39      0.63      0.48        52
   Basic       0.64      0.82      0.72        95
    Good       0.59      0.41      0.48        39

Excellent 0.00 0.00 0.00 1

accuracy 0.57 250

macro avg 0.52 0.42 0.42 250 weighted avg 0.67 0.57 0.55 250 | 0.4785 | | 0.5761 | 4.7163 | 1330 | 0.4768 | 0.5265 | 0.4278 | 0.4210 | 0.576 | precision recall f1-score support

    None       1.00      0.25      0.41        63
 Minimal       0.40      0.65      0.49        52
   Basic       0.64      0.82      0.72        95
    Good       0.59      0.41      0.48        39

Excellent 0.00 0.00 0.00 1

accuracy 0.58 250

macro avg 0.53 0.43 0.42 250 weighted avg 0.67 0.58 0.55 250 | 0.4768 | | 0.554 | 4.9645 | 1400 | 0.4772 | 0.5265 | 0.4278 | 0.4210 | 0.576 | precision recall f1-score support

    None       1.00      0.25      0.41        63
 Minimal       0.40      0.65      0.49        52
   Basic       0.64      0.82      0.72        95
    Good       0.59      0.41      0.48        39

Excellent 0.00 0.00 0.00 1

accuracy 0.58 250

macro avg 0.53 0.43 0.42 250 weighted avg 0.67 0.58 0.55 250 | 0.4772 |

Framework versions

Transformers 4.52.4
Pytorch 2.7.1
Datasets 3.6.0
Tokenizers 0.21.1

Davidozito/fewshot-2500-samples

fewshot-2500-samples

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Davidozito/
fewshot-2500-samples