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Davidozito
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fewshot-1000-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-1000-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-1000-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: 0.3018
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+ - Precision: 0.7134
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+ - Recall: 0.6660
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+ - F1 Macro: 0.6785
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+ - Accuracy: 0.69
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+ - Classification Report: precision recall f1-score support
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+
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+ None 0.77 0.53 0.62 19
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+ Minimal 0.57 0.77 0.66 26
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+ Basic 0.74 0.74 0.74 39
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+ Good 0.77 0.62 0.69 16
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+ Excellent 0.00 0.00 0.00 0
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+
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+ accuracy 0.69 100
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+ macro avg 0.57 0.53 0.54 100
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+ weighted avg 0.71 0.69 0.69 100
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+
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+ - Mse: 0.3018
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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 | 0.3009 | 0.7134 | 0.6660 | 0.6785 | 0.69 | precision recall f1-score support
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+
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+ None 0.77 0.53 0.62 19
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+ Minimal 0.57 0.77 0.66 26
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+ Basic 0.74 0.74 0.74 39
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+ Good 0.77 0.62 0.69 16
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+ Excellent 0.00 0.00 0.00 0
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+
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+ accuracy 0.69 100
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+ macro avg 0.57 0.53 0.54 100
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+ weighted avg 0.71 0.69 0.69 100
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+ | 0.3009 |
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+ | 0.5064 | 0.2478 | 28 | 0.3019 | 0.7056 | 0.6564 | 0.6705 | 0.68 | precision recall f1-score support
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+
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+ None 0.77 0.53 0.62 19
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+ Minimal 0.56 0.73 0.63 26
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+ Basic 0.72 0.74 0.73 39
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+ Good 0.77 0.62 0.69 16
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+ Excellent 0.00 0.00 0.00 0
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+
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+ accuracy 0.68 100
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+ macro avg 0.56 0.53 0.54 100
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+ weighted avg 0.70 0.68 0.68 100
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+ | 0.3019 |
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+ | 0.4732 | 0.4956 | 56 | 0.3032 | 0.7056 | 0.6564 | 0.6705 | 0.68 | precision recall f1-score support
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+
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+ None 0.77 0.53 0.62 19
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+ Minimal 0.56 0.73 0.63 26
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+ Basic 0.72 0.74 0.73 39
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+ Good 0.77 0.62 0.69 16
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+ Excellent 0.00 0.00 0.00 0
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+
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+ accuracy 0.68 100
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+ macro avg 0.56 0.53 0.54 100
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+ weighted avg 0.70 0.68 0.68 100
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+ | 0.3032 |
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+ | 0.512 | 0.7434 | 84 | 0.3092 | 0.6870 | 0.6433 | 0.6537 | 0.67 | precision recall f1-score support
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+
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+ None 0.75 0.47 0.58 19
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+ Minimal 0.56 0.73 0.63 26
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+ Basic 0.72 0.74 0.73 39
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+ Good 0.71 0.62 0.67 16
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+ Excellent 0.00 0.00 0.00 0
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+
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+ accuracy 0.67 100
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+ macro avg 0.55 0.51 0.52 100
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+ weighted avg 0.68 0.67 0.67 100
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+ | 0.3092 |
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+ | 0.4742 | 0.9912 | 112 | 0.3050 | 0.6968 | 0.6433 | 0.6569 | 0.67 | precision recall f1-score support
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+
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+ None 0.75 0.47 0.58 19
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+ Minimal 0.54 0.73 0.62 26
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+ Basic 0.72 0.74 0.73 39
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+ Good 0.77 0.62 0.69 16
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+ Excellent 0.00 0.00 0.00 0
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+
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+ accuracy 0.67 100
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+ macro avg 0.56 0.51 0.53 100
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+ weighted avg 0.69 0.67 0.67 100
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+ | 0.3050 |
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+ | 0.4689 | 1.2389 | 140 | 0.3080 | 0.6968 | 0.6433 | 0.6569 | 0.67 | precision recall f1-score support
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+
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+ None 0.75 0.47 0.58 19
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+ Minimal 0.54 0.73 0.62 26
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+ Basic 0.72 0.74 0.73 39
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+ Good 0.77 0.62 0.69 16
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+ Excellent 0.00 0.00 0.00 0
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+
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+ accuracy 0.67 100
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+ macro avg 0.56 0.51 0.53 100
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+ weighted avg 0.69 0.67 0.67 100
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+ | 0.3080 |
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+ | 0.5831 | 1.4867 | 168 | 0.3062 | 0.6968 | 0.6433 | 0.6569 | 0.67 | precision recall f1-score support
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+
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+ None 0.75 0.47 0.58 19
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+ Minimal 0.54 0.73 0.62 26
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+ Basic 0.72 0.74 0.73 39
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+ Good 0.77 0.62 0.69 16
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+ Excellent 0.00 0.00 0.00 0
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+
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+ accuracy 0.67 100
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+ macro avg 0.56 0.51 0.53 100
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+ weighted avg 0.69 0.67 0.67 100
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+ | 0.3062 |
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+ | 0.5038 | 1.7345 | 196 | 0.3018 | 0.7134 | 0.6660 | 0.6785 | 0.69 | precision recall f1-score support
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+
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+ Excellent 0.00 0.00 0.00 0
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+ macro avg 0.57 0.53 0.54 100
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+ weighted avg 0.71 0.69 0.69 100
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+ | 0.3018 |
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+ | 0.4657 | 1.9823 | 224 | 0.3053 | 0.6968 | 0.6433 | 0.6569 | 0.67 | precision recall f1-score support
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+
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+ Excellent 0.00 0.00 0.00 0
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+ accuracy 0.67 100
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+ macro avg 0.56 0.51 0.53 100
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+ weighted avg 0.69 0.67 0.67 100
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+ | 0.3053 |
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+ | 0.4269 | 2.2301 | 252 | 0.3086 | 0.6870 | 0.6433 | 0.6537 | 0.67 | precision recall f1-score support
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+ | 0.5885 | 2.7257 | 308 | 0.3090 | 0.6929 | 0.6497 | 0.6603 | 0.68 | precision recall f1-score support
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+ | 0.5033 | 3.2212 | 364 | 0.3088 | 0.7023 | 0.6653 | 0.6735 | 0.69 | precision recall f1-score support
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+ weighted avg 0.70 0.69 0.69 100
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+ | 0.3088 |
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+ | 0.4463 | 3.4690 | 392 | 0.3078 | 0.6813 | 0.6369 | 0.6471 | 0.66 | precision recall f1-score support
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+ macro avg 0.55 0.51 0.52 100
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+ weighted avg 0.68 0.66 0.66 100
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+ | 0.3076 |
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+ | 0.5586 | 4.4602 | 504 | 0.3079 | 0.6870 | 0.6433 | 0.6537 | 0.67 | precision recall f1-score support
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+
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+ Excellent 0.00 0.00 0.00 0
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+
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+ accuracy 0.67 100
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+ macro avg 0.55 0.51 0.52 100
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+ weighted avg 0.68 0.67 0.67 100
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+ | 0.3079 |
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+ | 0.4148 | 4.7080 | 532 | 0.3082 | 0.6964 | 0.6589 | 0.6668 | 0.68 | precision recall f1-score support
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+
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+ Excellent 0.00 0.00 0.00 0
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+ | 0.3082 |
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+ | 0.4852 | 4.9558 | 560 | 0.3086 | 0.7023 | 0.6653 | 0.6735 | 0.69 | precision recall f1-score support
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+ accuracy 0.69 100
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+ weighted avg 0.70 0.69 0.69 100
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+ | 0.3086 |
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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,
41
+ "special": true
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+ }
43
+ },
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+ "bos_token": "<s>",
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+ "clean_up_tokenization_spaces": false,
46
+ "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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+ "model_max_length": 512,
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+ "pad_token": "<pad>",
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+ "sep_token": "</s>",
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+ "stride": 0,
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+ "tokenizer_class": "XLMRobertaTokenizer",
56
+ "truncation_side": "right",
57
+ "truncation_strategy": "longest_first",
58
+ "unk_token": "<unk>"
59
+ }
training_args.bin ADDED
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