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End of training

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: projecte-aina/roberta-base-ca-v2-cased-te
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: SYN_300524_epoch_1
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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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+ # SYN_300524_epoch_1
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+
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+ This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3748
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+ - Accuracy: 0.961
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+ - Precision: 0.9615
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+ - Recall: 0.961
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+ - F1: 0.9610
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+ - Ratio: 0.483
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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: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 47
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 32
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.06
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+ - lr_scheduler_warmup_steps: 4
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+ - num_epochs: 1
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+ - label_smoothing_factor: 0.1
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----:|
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+ | 2.9367 | 0.0533 | 10 | 1.4668 | 0.65 | 0.7117 | 0.65 | 0.6225 | 0.77 |
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+ | 1.1009 | 0.1067 | 20 | 0.6674 | 0.856 | 0.8560 | 0.856 | 0.8560 | 0.502 |
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+ | 0.6993 | 0.16 | 30 | 0.5583 | 0.908 | 0.9095 | 0.9080 | 0.9079 | 0.53 |
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+ | 0.6377 | 0.2133 | 40 | 0.4923 | 0.934 | 0.9343 | 0.9340 | 0.9340 | 0.486 |
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+ | 0.5192 | 0.2667 | 50 | 0.4930 | 0.926 | 0.9282 | 0.9260 | 0.9259 | 0.464 |
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+ | 0.5189 | 0.32 | 60 | 0.4687 | 0.937 | 0.9383 | 0.937 | 0.9370 | 0.527 |
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+ | 0.5083 | 0.3733 | 70 | 0.4321 | 0.944 | 0.9445 | 0.944 | 0.9440 | 0.484 |
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+ | 0.4645 | 0.4267 | 80 | 0.4026 | 0.949 | 0.9490 | 0.949 | 0.9490 | 0.495 |
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+ | 0.4268 | 0.48 | 90 | 0.3990 | 0.949 | 0.9498 | 0.9490 | 0.9490 | 0.479 |
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+ | 0.4327 | 0.5333 | 100 | 0.3949 | 0.952 | 0.9524 | 0.952 | 0.9520 | 0.486 |
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+ | 0.4283 | 0.5867 | 110 | 0.3894 | 0.954 | 0.9540 | 0.954 | 0.9540 | 0.496 |
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+ | 0.4263 | 0.64 | 120 | 0.3829 | 0.957 | 0.9572 | 0.957 | 0.9570 | 0.489 |
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+ | 0.4205 | 0.6933 | 130 | 0.3800 | 0.962 | 0.9620 | 0.962 | 0.9620 | 0.496 |
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+ | 0.4291 | 0.7467 | 140 | 0.3760 | 0.962 | 0.9620 | 0.962 | 0.9620 | 0.502 |
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+ | 0.4124 | 0.8 | 150 | 0.3723 | 0.964 | 0.9641 | 0.964 | 0.9640 | 0.494 |
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+ | 0.4142 | 0.8533 | 160 | 0.3720 | 0.964 | 0.9641 | 0.964 | 0.9640 | 0.492 |
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+ | 0.4209 | 0.9067 | 170 | 0.3767 | 0.96 | 0.9605 | 0.96 | 0.9600 | 0.484 |
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+ | 0.3908 | 0.96 | 180 | 0.3748 | 0.96 | 0.9605 | 0.96 | 0.9600 | 0.484 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.1
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+ - Pytorch 2.3.0+cu121
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
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