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Artigenz-Coder-DS-6.7B_Fi__components_size_252_epochs_10_2024-06-21_09-34-51_3556543

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+ ---
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+ license: other
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+ base_model: Artigenz/Artigenz-Coder-DS-6.7B
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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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+ - bleu
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+ - sacrebleu
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+ - rouge
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+ model-index:
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+ - name: Artigenz-Coder-DS-6.7B_Fi__components_size_252_epochs_10_2024-06-21_09-34-51_3556543
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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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+ # Artigenz-Coder-DS-6.7B_Fi__components_size_252_epochs_10_2024-06-21_09-34-51_3556543
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+
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+ This model is a fine-tuned version of [Artigenz/Artigenz-Coder-DS-6.7B](https://huggingface.co/Artigenz/Artigenz-Coder-DS-6.7B) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 3.6318
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+ - Accuracy: 0.48
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+ - Chrf: 0.022
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+ - Bleu: 0.0
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+ - Sacrebleu: 0.0
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+ - Rouge1: 0.0
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+ - Rouge2: 0.0
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+ - Rougel: 0.0
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+ - Rougelsum: 0.0
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+ - Meteor: 0.099
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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: 0.001
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+ - train_batch_size: 1
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+ - eval_batch_size: 1
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+ - seed: 3407
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+ - distributed_type: multi-GPU
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+ - num_devices: 4
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+ - total_train_batch_size: 4
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+ - total_eval_batch_size: 4
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 252
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+ - training_steps: 2520
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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 | Chrf | Bleu | Sacrebleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:|
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+ | 0.0255 | 4.0 | 252 | 1.2068 | 0.468 | 0.574 | 0.429 | 0.4 | 0.507 | 0.325 | 0.458 | 0.504 | 0.56 |
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+ | 0.0989 | 8.0 | 504 | 3.8823 | 0.479 | 0.026 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.143 |
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+ | 0.1071 | 12.0 | 756 | 3.8516 | 0.477 | 0.036 | 0.036 | 0.0 | 0.158 | 0.079 | 0.155 | 0.158 | 0.135 |
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+ | 1.382 | 16.0 | 1008 | 3.7440 | 0.485 | 0.046 | 0.016 | 0.0 | 0.159 | 0.094 | 0.159 | 0.159 | 0.16 |
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+ | 0.2463 | 20.0 | 1260 | 3.8049 | 0.48 | 0.04 | 0.0 | 0.0 | 0.066 | 0.033 | 0.066 | 0.066 | 0.139 |
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+ | 0.6094 | 24.0 | 1512 | 3.9803 | 0.446 | 0.021 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.069 |
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+ | 0.0514 | 28.0 | 1764 | 3.7417 | 0.48 | 0.023 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.104 |
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+ | 0.2303 | 32.0 | 2016 | 3.6727 | 0.48 | 0.015 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.033 |
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+ | 0.064 | 36.0 | 2268 | 3.6537 | 0.48 | 0.02 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.084 |
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+ | 0.0697 | 40.0 | 2520 | 3.6318 | 0.48 | 0.022 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.099 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.37.0
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.20.0
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+ - Tokenizers 0.15.2