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Davidozito/fewshot-1000-samples

sourceHugging Faceupdated 1y agoView on Hugging Face

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fewshot-1000-samples

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

—Loss: 0.4642
—Precision: 0.7370
—Recall: 0.5023
—F1 Macro: 0.4540
—Accuracy: 0.61
—Classification Report: precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.51 0.92 0.66 26 Basic 0.69 0.85 0.76 39 Good 0.75 0.19 0.30 16 Excellent 0.00 0.00 0.00 0

accuracy 0.61 100 macro avg 0.59 0.40 0.36 100 weighted avg 0.71 0.61 0.53 100

—Mse: 0.4642

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
—trainbatchsize: 8
—evalbatchsize: 8
—seed: 42
—optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
—lrschedulertype: linear
—lrschedulerwarmup_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	0.4665	0.7083	0.4803	0.4237	0.59	precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.50 0.92 0.65 26 Basic 0.67 0.82 0.74 39 Good 0.67 0.12 0.21 16 Excellent 0.00 0.00 0.00 0

accuracy 0.59 100 macro avg 0.57 0.38 0.34 100 weighted avg 0.69 0.59 0.51 100 | 0.4665 | | 0.6442 | 0.2478 | 28 | 0.4682 | 0.7370 | 0.5023 | 0.4540 | 0.61 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.51 0.92 0.66 26 Basic 0.69 0.85 0.76 39 Good 0.75 0.19 0.30 16 Excellent 0.00 0.00 0.00 0

accuracy 0.61 100 macro avg 0.59 0.40 0.36 100 weighted avg 0.71 0.61 0.53 100 | 0.4682 | | 0.6338 | 0.4956 | 56 | 0.4693 | 0.7495 | 0.5116 | 0.4741 | 0.62 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.51 0.85 0.64 26 Basic 0.69 0.90 0.78 39 Good 0.80 0.25 0.38 16 Excellent 0.00 0.00 0.00 0

accuracy 0.62 100 macro avg 0.60 0.41 0.38 100 weighted avg 0.72 0.62 0.55 100 | 0.4693 | | 0.6766 | 0.7434 | 84 | 0.4725 | 0.7358 | 0.4887 | 0.4653 | 0.59 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.46 0.69 0.55 26 Basic 0.65 0.90 0.75 39 Good 0.83 0.31 0.45 16 Excellent 0.00 0.00 0.00 0

accuracy 0.59 100 macro avg 0.59 0.39 0.37 100 weighted avg 0.70 0.59 0.53 100 | 0.4725 | | 0.5747 | 0.9912 | 112 | 0.4642 | 0.7370 | 0.5023 | 0.4540 | 0.61 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.51 0.92 0.66 26 Basic 0.69 0.85 0.76 39 Good 0.75 0.19 0.30 16 Excellent 0.00 0.00 0.00 0

accuracy 0.61 100 macro avg 0.59 0.40 0.36 100 weighted avg 0.71 0.61 0.53 100 | 0.4642 | | 0.615 | 1.2389 | 140 | 0.4665 | 0.7422 | 0.4983 | 0.4728 | 0.6 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.47 0.73 0.58 26 Basic 0.66 0.90 0.76 39 Good 0.83 0.31 0.45 16 Excellent 0.00 0.00 0.00 0

accuracy 0.60 100 macro avg 0.59 0.40 0.38 100 weighted avg 0.70 0.60 0.54 100 | 0.4665 | | 0.6894 | 1.4867 | 168 | 0.4721 | 0.7503 | 0.5168 | 0.4993 | 0.61 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.46 0.65 0.54 26 Basic 0.67 0.92 0.77 39 Good 0.88 0.44 0.58 16 Excellent 0.00 0.00 0.00 0

accuracy 0.61 100 macro avg 0.60 0.41 0.40 100 weighted avg 0.71 0.61 0.55 100 | 0.4721 | | 0.6161 | 1.7345 | 196 | 0.4684 | 0.7494 | 0.5108 | 0.4896 | 0.61 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.47 0.69 0.56 26 Basic 0.67 0.92 0.77 39 Good 0.86 0.38 0.52 16 Excellent 0.00 0.00 0.00 0

accuracy 0.61 100 macro avg 0.60 0.41 0.39 100 weighted avg 0.71 0.61 0.55 100 | 0.4684 | | 0.5899 | 1.9823 | 224 | 0.4733 | 0.7503 | 0.5168 | 0.4993 | 0.61 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.46 0.65 0.54 26 Basic 0.67 0.92 0.77 39 Good 0.88 0.44 0.58 16 Excellent 0.00 0.00 0.00 0

accuracy 0.61 100 macro avg 0.60 0.41 0.40 100 weighted avg 0.71 0.61 0.55 100 | 0.4733 | | 0.571 | 2.2301 | 252 | 0.4785 | 0.7438 | 0.5071 | 0.4915 | 0.6 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.46 0.62 0.52 26 Basic 0.64 0.92 0.76 39 Good 0.88 0.44 0.58 16 Excellent 0.00 0.00 0.00 0

accuracy 0.60 100 macro avg 0.59 0.41 0.39 100 weighted avg 0.70 0.60 0.54 100 | 0.4785 | | 0.6233 | 2.4779 | 280 | 0.4788 | 0.7438 | 0.5071 | 0.4915 | 0.6 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.46 0.62 0.52 26 Basic 0.64 0.92 0.76 39 Good 0.88 0.44 0.58 16 Excellent 0.00 0.00 0.00 0

accuracy 0.60 100 macro avg 0.59 0.41 0.39 100 weighted avg 0.70 0.60 0.54 100 | 0.4788 | | 0.7254 | 2.7257 | 308 | 0.4766 | 0.7438 | 0.5071 | 0.4915 | 0.6 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.46 0.62 0.52 26 Basic 0.64 0.92 0.76 39 Good 0.88 0.44 0.58 16 Excellent 0.00 0.00 0.00 0

accuracy 0.60 100 macro avg 0.59 0.41 0.39 100 weighted avg 0.70 0.60 0.54 100 | 0.4766 | | 0.6445 | 2.9735 | 336 | 0.4723 | 0.7504 | 0.5168 | 0.4994 | 0.61 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.47 0.65 0.55 26 Basic 0.65 0.92 0.77 39 Good 0.88 0.44 0.58 16 Excellent 0.00 0.00 0.00 0

accuracy 0.61 100 macro avg 0.60 0.41 0.40 100 weighted avg 0.71 0.61 0.55 100 | 0.4723 | | 0.6215 | 3.2212 | 364 | 0.4683 | 0.7503 | 0.5168 | 0.4993 | 0.61 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.46 0.65 0.54 26 Basic 0.67 0.92 0.77 39 Good 0.88 0.44 0.58 16 Excellent 0.00 0.00 0.00 0

accuracy 0.61 100 macro avg 0.60 0.41 0.40 100 weighted avg 0.71 0.61 0.55 100 | 0.4683 | | 0.5525 | 3.4690 | 392 | 0.4684 | 0.7504 | 0.5168 | 0.4994 | 0.61 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.47 0.65 0.55 26 Basic 0.65 0.92 0.77 39 Good 0.88 0.44 0.58 16 Excellent 0.00 0.00 0.00 0

accuracy 0.61 100 macro avg 0.60 0.41 0.40 100 weighted avg 0.71 0.61 0.55 100 | 0.4684 | | 0.6701 | 3.7168 | 420 | 0.4699 | 0.7504 | 0.5168 | 0.4994 | 0.61 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.47 0.65 0.55 26 Basic 0.65 0.92 0.77 39 Good 0.88 0.44 0.58 16 Excellent 0.00 0.00 0.00 0

accuracy 0.61 100 macro avg 0.60 0.41 0.40 100 weighted avg 0.71 0.61 0.55 100 | 0.4699 | | 0.5554 | 3.9646 | 448 | 0.4724 | 0.7438 | 0.5071 | 0.4915 | 0.6 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.46 0.62 0.52 26 Basic 0.64 0.92 0.76 39 Good 0.88 0.44 0.58 16 Excellent 0.00 0.00 0.00 0

accuracy 0.60 100 macro avg 0.59 0.41 0.39 100 weighted avg 0.70 0.60 0.54 100 | 0.4724 | | 0.615 | 4.2124 | 476 | 0.4698 | 0.7504 | 0.5168 | 0.4994 | 0.61 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.47 0.65 0.55 26 Basic 0.65 0.92 0.77 39 Good 0.88 0.44 0.58 16 Excellent 0.00 0.00 0.00 0

accuracy 0.61 100 macro avg 0.60 0.41 0.40 100 weighted avg 0.71 0.61 0.55 100 | 0.4698 | | 0.678 | 4.4602 | 504 | 0.4706 | 0.7504 | 0.5168 | 0.4994 | 0.61 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.47 0.65 0.55 26 Basic 0.65 0.92 0.77 39 Good 0.88 0.44 0.58 16 Excellent 0.00 0.00 0.00 0

accuracy 0.61 100 macro avg 0.60 0.41 0.40 100 weighted avg 0.71 0.61 0.55 100 | 0.4706 | | 0.5355 | 4.7080 | 532 | 0.4711 | 0.7438 | 0.5071 | 0.4915 | 0.6 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.46 0.62 0.52 26 Basic 0.64 0.92 0.76 39 Good 0.88 0.44 0.58 16 Excellent 0.00 0.00 0.00 0

accuracy 0.60 100 macro avg 0.59 0.41 0.39 100 weighted avg 0.70 0.60 0.54 100 | 0.4711 | | 0.6137 | 4.9558 | 560 | 0.4716 | 0.7438 | 0.5071 | 0.4915 | 0.6 | precision recall f1-score support

None 1.00 0.05 0.10 19 Minimal 0.46 0.62 0.52 26 Basic 0.64 0.92 0.76 39 Good 0.88 0.44 0.58 16 Excellent 0.00 0.00 0.00 0

accuracy 0.60 100 macro avg 0.59 0.41 0.39 100 weighted avg 0.70 0.60 0.54 100 | 0.4716 |

Framework versions

—Transformers 4.52.4
—Pytorch 2.7.1
—Datasets 3.6.0
—Tokenizers 0.21.1