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fewshot-2500-samples
Davidozito/fewshot-2500-samples
fewshot-2500-samples is a text classification model from Davidozito. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- xlm-roberta
- Access
- Public
- Created
- Jun 17, 2025
- Updated
- Jun 17, 2025
- SHA
- 8a034a9e
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Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
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
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Recall: 0.4151
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F1 Macro: 0.4036
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Accuracy: 0.58
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Classification Report: precision recall f1-score support
None 1.00 0.27 0.42 63Minimal 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
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- Created
- Jun 17, 2025
- Updated
- Jun 17, 2025