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fewshot-1000-samples
Davidozito/fewshot-1000-samples
fewshot-1000-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
- 97fc01e4
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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-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
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Recall: 0.5023
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F1 Macro: 0.4540
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Accuracy: 0.61
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Classification Report: precision recall f1-score support
None 1.00 0.05 0.10 19Minimal 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
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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
- 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 | 0.4665 | 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
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- Created
- Jun 17, 2025
- Updated
- Jun 17, 2025