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Davidozito/
fewshot-250-samples

Name: fewshot-250-samples
Availability: InStock

generated_from_trainer

autotrain_compatible

endpoints_compatible

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fewshot-250-samples

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

Loss: 0.4952
Precision: 0.8199
Recall: 0.5655
F1 Macro: 0.5981
Accuracy: 0.64
Classification Report: precision recall f1-score support

None 1.00 0.33 0.50 3

Minimal 0.75 0.50 0.60 6 Basic 0.53 1.00 0.69 9 Good 1.00 0.43 0.60 7 Excellent 0.00 0.00 0.00 0

accuracy 0.64 25 macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25
Mse: 0.4952

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	0.5164	0.7896	0.5496	0.5327	0.6	precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.62      0.83      0.71         6
   Basic       0.53      0.89      0.67         9
    Good       1.00      0.14      0.25         7

Excellent 0.00 0.00 0.00 0

accuracy 0.60 25

macro avg 0.63 0.44 0.43 25 weighted avg 0.74 0.60 0.54 25 | 0.5164 | | 0.5593 | 0.2414 | 7 | 0.5152 | 0.7896 | 0.5496 | 0.5327 | 0.6 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.62      0.83      0.71         6
   Basic       0.53      0.89      0.67         9
    Good       1.00      0.14      0.25         7

Excellent 0.00 0.00 0.00 0

accuracy 0.60 25

macro avg 0.63 0.44 0.43 25 weighted avg 0.74 0.60 0.54 25 | 0.5152 | | 0.8353 | 0.4828 | 14 | 0.5129 | 0.8192 | 0.5774 | 0.5598 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.71      0.83      0.77         6
   Basic       0.56      1.00      0.72         9
    Good       1.00      0.14      0.25         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.46 0.45 25 weighted avg 0.77 0.64 0.57 25 | 0.5129 | | 0.7268 | 0.7241 | 21 | 0.5070 | 0.8324 | 0.5714 | 0.5910 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.80      0.67      0.73         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.29      0.44         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.67 0.46 0.47 25 weighted avg 0.78 0.64 0.61 25 | 0.5070 | | 0.8386 | 0.9655 | 28 | 0.5012 | 0.8324 | 0.5714 | 0.5910 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.80      0.67      0.73         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.29      0.44         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.67 0.46 0.47 25 weighted avg 0.78 0.64 0.61 25 | 0.5012 | | 0.866 | 1.2069 | 35 | 0.4989 | 0.8324 | 0.5714 | 0.5910 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.80      0.67      0.73         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.29      0.44         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.67 0.46 0.47 25 weighted avg 0.78 0.64 0.61 25 | 0.4989 | | 0.674 | 1.4483 | 42 | 0.4983 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4983 | | 0.607 | 1.6897 | 49 | 0.4982 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4982 | | 0.5297 | 1.9310 | 56 | 0.4981 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4981 | | 0.6795 | 2.1724 | 63 | 0.4978 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4978 | | 0.7007 | 2.4138 | 70 | 0.4974 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4974 | | 0.6341 | 2.6552 | 77 | 0.4974 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4974 | | 0.7763 | 2.8966 | 84 | 0.4970 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4970 | | 0.8144 | 3.1379 | 91 | 0.4965 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4965 | | 0.7211 | 3.3793 | 98 | 0.4963 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4963 | | 0.5704 | 3.6207 | 105 | 0.4961 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4961 | | 0.7294 | 3.8621 | 112 | 0.4960 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4960 | | 0.8442 | 4.1034 | 119 | 0.4958 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4958 | | 0.7277 | 4.3448 | 126 | 0.4956 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4956 | | 0.607 | 4.5862 | 133 | 0.4953 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4953 | | 0.6661 | 4.8276 | 140 | 0.4952 | 0.8199 | 0.5655 | 0.5981 | 0.64 | precision recall f1-score support

    None       1.00      0.33      0.50         3
 Minimal       0.75      0.50      0.60         6
   Basic       0.53      1.00      0.69         9
    Good       1.00      0.43      0.60         7

Excellent 0.00 0.00 0.00 0

accuracy 0.64 25

macro avg 0.66 0.45 0.48 25 weighted avg 0.77 0.64 0.62 25 | 0.4952 |

Framework versions

Transformers 4.52.4
Pytorch 2.7.1
Datasets 3.6.0
Tokenizers 0.21.1

Davidozito/fewshot-250-samples

fewshot-250-samples

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Davidozito/
fewshot-250-samples