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Davidozito
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fewshot-2500-samples

Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Model card Files
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End of training

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Files changed (9) hide show
  1. .gitattributes +1 -0
  2. README.md +329 -0
  3. config.json +34 -0
  4. model.safetensors +3 -0
  5. sentencepiece.bpe.model +3 -0
  6. special_tokens_map.json +51 -0
  7. tokenizer.json +3 -0
  8. tokenizer_config.json +59 -0
  9. training_args.bin +3 -0
.gitattributes CHANGED
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  *.zst filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ library_name: transformers
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - accuracy
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+ model-index:
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+ - name: fewshot-2500-samples
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # fewshot-2500-samples
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+
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+ This model was trained from scratch on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3100
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+ - Precision: 0.5575
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+ - Recall: 0.5497
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+ - F1 Macro: 0.5438
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+ - Accuracy: 0.688
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+ - Classification Report: precision recall f1-score support
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+
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+ None 0.95 0.67 0.79 63
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+ Minimal 0.57 0.81 0.67 52
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+ Basic 0.73 0.68 0.71 95
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+ Good 0.53 0.59 0.56 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.69 250
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+ macro avg 0.56 0.55 0.54 250
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+ weighted avg 0.72 0.69 0.69 250
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+
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+ - Mse: 0.3100
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy | Classification Report | Mse |
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+ |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------:|
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+ | No log | 0 | 0 | 0.3048 | 0.5593 | 0.5507 | 0.5454 | 0.688 | precision recall f1-score support
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+
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+ None 0.96 0.68 0.80 63
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+ Minimal 0.55 0.81 0.66 52
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+ Basic 0.73 0.67 0.70 95
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+ Good 0.56 0.59 0.57 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.69 250
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+ macro avg 0.56 0.55 0.55 250
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+ weighted avg 0.72 0.69 0.69 250
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+ | 0.3048 |
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+ | 0.4757 | 0.2482 | 70 | 0.3100 | 0.5575 | 0.5497 | 0.5438 | 0.688 | precision recall f1-score support
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+
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+ None 0.95 0.67 0.79 63
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+ Minimal 0.57 0.81 0.67 52
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+ Basic 0.73 0.68 0.71 95
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+ Good 0.53 0.59 0.56 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.69 250
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+ macro avg 0.56 0.55 0.54 250
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+ weighted avg 0.72 0.69 0.69 250
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+ | 0.3100 |
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+ | 0.509 | 0.4965 | 140 | 0.3359 | 0.5631 | 0.5404 | 0.5248 | 0.664 | precision recall f1-score support
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+
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+ None 1.00 0.49 0.66 63
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+ Minimal 0.51 0.81 0.63 52
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+ Basic 0.76 0.68 0.72 95
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+ Good 0.54 0.72 0.62 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.66 250
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+ macro avg 0.56 0.54 0.52 250
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+ weighted avg 0.73 0.66 0.67 250
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+ | 0.3359 |
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+ | 0.4873 | 0.7447 | 210 | 0.3170 | 0.5538 | 0.5400 | 0.5321 | 0.672 | precision recall f1-score support
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+
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+ None 0.97 0.60 0.75 63
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+ Minimal 0.55 0.81 0.65 52
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+ Basic 0.73 0.67 0.70 95
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+ Good 0.52 0.62 0.56 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.67 250
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+ macro avg 0.55 0.54 0.53 250
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+ weighted avg 0.72 0.67 0.68 250
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+ | 0.3170 |
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+ | 0.4755 | 0.9929 | 280 | 0.3264 | 0.5577 | 0.5409 | 0.5296 | 0.668 | precision recall f1-score support
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+
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+ None 0.97 0.54 0.69 63
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+ Minimal 0.52 0.79 0.63 52
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+ Basic 0.75 0.68 0.71 95
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+ Good 0.55 0.69 0.61 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.67 250
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+ macro avg 0.56 0.54 0.53 250
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+ weighted avg 0.72 0.67 0.67 250
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+ | 0.3264 |
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+ | 0.5092 | 1.2411 | 350 | 0.3452 | 0.5616 | 0.5374 | 0.5201 | 0.66 | precision recall f1-score support
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+
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+ None 1.00 0.46 0.63 63
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+ Minimal 0.51 0.79 0.62 52
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+ Basic 0.76 0.69 0.73 95
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+ Good 0.54 0.74 0.62 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.66 250
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+ macro avg 0.56 0.54 0.52 250
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+ weighted avg 0.73 0.66 0.66 250
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+ | 0.3452 |
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+ | 0.4668 | 1.4894 | 420 | 0.3257 | 0.5577 | 0.5409 | 0.5296 | 0.668 | precision recall f1-score support
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+
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+ None 0.97 0.54 0.69 63
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+ Minimal 0.52 0.79 0.63 52
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+ Basic 0.75 0.68 0.71 95
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+ Good 0.55 0.69 0.61 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.67 250
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+ macro avg 0.56 0.54 0.53 250
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+ weighted avg 0.72 0.67 0.67 250
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+ | 0.3257 |
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+ | 0.4722 | 1.7376 | 490 | 0.3101 | 0.5612 | 0.5548 | 0.5481 | 0.692 | precision recall f1-score support
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+
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+ None 0.95 0.67 0.79 63
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+ Minimal 0.57 0.81 0.67 52
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+ Basic 0.74 0.68 0.71 95
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+ Good 0.55 0.62 0.58 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.69 250
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+ macro avg 0.56 0.55 0.55 250
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+ weighted avg 0.72 0.69 0.70 250
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+ | 0.3101 |
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+ | 0.4893 | 1.9858 | 560 | 0.3318 | 0.5565 | 0.5409 | 0.5286 | 0.668 | precision recall f1-score support
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+
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+ None 0.97 0.54 0.69 63
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+ Minimal 0.53 0.79 0.63 52
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+ Basic 0.76 0.68 0.72 95
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+ Good 0.53 0.69 0.60 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.67 250
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+ macro avg 0.56 0.54 0.53 250
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+ weighted avg 0.72 0.67 0.67 250
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+ | 0.3318 |
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+ | 0.4356 | 2.2340 | 630 | 0.3189 | 0.5611 | 0.5502 | 0.5402 | 0.68 | precision recall f1-score support
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+
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+ None 0.97 0.60 0.75 63
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+ Minimal 0.55 0.81 0.65 52
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+ Basic 0.74 0.67 0.71 95
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+ Good 0.54 0.67 0.60 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.68 250
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+ macro avg 0.56 0.55 0.54 250
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+ weighted avg 0.73 0.68 0.69 250
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+ | 0.3189 |
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+ | 0.4772 | 2.4823 | 700 | 0.3294 | 0.5591 | 0.5430 | 0.5313 | 0.672 | precision recall f1-score support
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+
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+ None 0.97 0.54 0.69 63
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+ Minimal 0.53 0.79 0.63 52
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+ Basic 0.76 0.69 0.73 95
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+ Good 0.54 0.69 0.61 39
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+ Excellent 0.00 0.00 0.00 1
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+ accuracy 0.67 250
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+ macro avg 0.56 0.54 0.53 250
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+ weighted avg 0.73 0.67 0.68 250
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+ | 0.3294 |
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+ | 0.4249 | 2.7305 | 770 | 0.3243 | 0.5607 | 0.5473 | 0.5367 | 0.676 | precision recall f1-score support
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+
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+ None 0.97 0.57 0.72 63
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+ Minimal 0.53 0.79 0.64 52
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+ Basic 0.75 0.68 0.71 95
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+ Good 0.55 0.69 0.61 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.68 250
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+ macro avg 0.56 0.55 0.54 250
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+ weighted avg 0.73 0.68 0.68 250
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+ | 0.3243 |
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+ | 0.5383 | 2.9787 | 840 | 0.3318 | 0.5575 | 0.5440 | 0.5297 | 0.668 | precision recall f1-score support
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+
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+ None 0.97 0.54 0.69 63
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+ Minimal 0.53 0.79 0.63 52
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+ Basic 0.76 0.67 0.72 95
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+ Good 0.53 0.72 0.61 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.67 250
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+ macro avg 0.56 0.54 0.53 250
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+ weighted avg 0.73 0.67 0.67 250
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+ | 0.3318 |
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+ | 0.4388 | 3.2270 | 910 | 0.3394 | 0.5628 | 0.5417 | 0.5253 | 0.664 | precision recall f1-score support
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+
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+ None 1.00 0.49 0.66 63
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+ Minimal 0.51 0.79 0.62 52
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+ Basic 0.76 0.68 0.72 95
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+ Good 0.54 0.74 0.62 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.66 250
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+ macro avg 0.56 0.54 0.53 250
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+ weighted avg 0.73 0.66 0.67 250
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+ | 0.3394 |
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+ | 0.4973 | 3.4752 | 980 | 0.3308 | 0.5565 | 0.5409 | 0.5286 | 0.668 | precision recall f1-score support
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+
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+ None 0.97 0.54 0.69 63
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+ Minimal 0.53 0.79 0.63 52
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+ Basic 0.76 0.68 0.72 95
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+ Good 0.53 0.69 0.60 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.67 250
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+ macro avg 0.56 0.54 0.53 250
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+ weighted avg 0.72 0.67 0.67 250
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+ | 0.3308 |
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+ | 0.5125 | 3.7234 | 1050 | 0.3361 | 0.5606 | 0.5397 | 0.5253 | 0.664 | precision recall f1-score support
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+
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+ None 1.00 0.51 0.67 63
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+ Minimal 0.52 0.79 0.63 52
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+ Basic 0.76 0.68 0.72 95
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+ Good 0.53 0.72 0.61 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.66 250
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+ macro avg 0.56 0.54 0.53 250
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+ weighted avg 0.73 0.66 0.67 250
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+ | 0.3361 |
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+ | 0.4262 | 3.9716 | 1120 | 0.3230 | 0.5637 | 0.5511 | 0.5396 | 0.68 | precision recall f1-score support
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+
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+ None 0.97 0.57 0.72 63
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+ Minimal 0.54 0.81 0.65 52
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+ Basic 0.76 0.68 0.72 95
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+ Good 0.55 0.69 0.61 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.68 250
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+ macro avg 0.56 0.55 0.54 250
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+ weighted avg 0.73 0.68 0.68 250
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+ | 0.3230 |
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+ | 0.4915 | 4.2199 | 1190 | 0.3240 | 0.5607 | 0.5473 | 0.5367 | 0.676 | precision recall f1-score support
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+
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+ None 0.97 0.57 0.72 63
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+ Minimal 0.53 0.79 0.64 52
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+ Basic 0.75 0.68 0.71 95
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+ Good 0.55 0.69 0.61 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.68 250
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+ macro avg 0.56 0.55 0.54 250
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+ weighted avg 0.73 0.68 0.68 250
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+ | 0.3240 |
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+ | 0.4515 | 4.4681 | 1260 | 0.3260 | 0.5592 | 0.5441 | 0.5332 | 0.672 | precision recall f1-score support
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+
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+ None 0.97 0.56 0.71 63
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+ Minimal 0.53 0.79 0.63 52
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+ Basic 0.75 0.68 0.71 95
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+ Good 0.55 0.69 0.61 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.67 250
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+ macro avg 0.56 0.54 0.53 250
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+ weighted avg 0.72 0.67 0.68 250
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+ | 0.3260 |
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+ | 0.4823 | 4.7163 | 1330 | 0.3248 | 0.5607 | 0.5473 | 0.5367 | 0.676 | precision recall f1-score support
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+
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+ None 0.97 0.57 0.72 63
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+ Minimal 0.53 0.79 0.64 52
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+ Basic 0.75 0.68 0.71 95
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+ Good 0.55 0.69 0.61 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.68 250
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+ macro avg 0.56 0.55 0.54 250
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+ weighted avg 0.73 0.68 0.68 250
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+ | 0.3248 |
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+ | 0.4394 | 4.9645 | 1400 | 0.3258 | 0.5607 | 0.5473 | 0.5367 | 0.676 | precision recall f1-score support
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+
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+ None 0.97 0.57 0.72 63
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+ Minimal 0.53 0.79 0.64 52
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+ Basic 0.75 0.68 0.71 95
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+ Good 0.55 0.69 0.61 39
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+ Excellent 0.00 0.00 0.00 1
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+
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+ accuracy 0.68 250
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+ macro avg 0.56 0.55 0.54 250
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+ weighted avg 0.73 0.68 0.68 250
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+ | 0.3258 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.52.4
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+ - Pytorch 2.7.1
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+ - Datasets 3.6.0
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+ - Tokenizers 0.21.1
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+ "single_word": false,
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+ "special": true
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+ }
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+ "bos_token": "<s>",
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+ "clean_up_tokenization_spaces": false,
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+ "cls_token": "<s>",
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+ "eos_token": "</s>",
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+ "extra_special_tokens": {},
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+ "mask_token": "<mask>",
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+ "max_length": 512,
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+ "tokenizer_class": "XLMRobertaTokenizer",
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+ "truncation_side": "right",
57
+ "truncation_strategy": "longest_first",
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+ "unk_token": "<unk>"
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+ }
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