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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: HuggingFaceTB/SmolLM2-135M |
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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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model-index: |
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- name: smol-135-tq-closure-augment-synthetic |
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results: [] |
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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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# smol-135-tq-closure-augment-synthetic |
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This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1898 |
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- < Precision: 0.9121 |
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- < Recall: 0.9051 |
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- < F1-score: 0.9086 |
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- < Support: 7717.0 |
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- > Precision: 0.9113 |
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- > Recall: 0.9016 |
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- > F1-score: 0.9065 |
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- > Support: 7717.0 |
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- = Precision: 0.7992 |
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- = Recall: 0.8098 |
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- = F1-score: 0.8045 |
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- = Support: 3244.0 |
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- - Precision: 0.7401 |
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- - Recall: 0.7950 |
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- - F1-score: 0.7666 |
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- - Support: 1322.0 |
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- Accuracy: 0.8810 |
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- Macro Avg Precision: 0.8407 |
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- Macro Avg Recall: 0.8529 |
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- Macro Avg F1-score: 0.8465 |
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- Macro Avg Support: 20000.0 |
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- Weighted Avg Precision: 0.8821 |
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- Weighted Avg Recall: 0.8810 |
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- Weighted Avg F1-score: 0.8815 |
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- Weighted Avg Support: 20000.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 512 |
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- total_eval_batch_size: 256 |
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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: reduce_lr_on_plateau |
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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | < Precision | < Recall | < F1-score | < Support | > Precision | > Recall | > F1-score | > Support | = Precision | = Recall | = F1-score | = Support | - Precision | - Recall | - F1-score | - Support | Accuracy | Macro Avg Precision | Macro Avg Recall | Macro Avg F1-score | Macro Avg Support | Weighted Avg Precision | Weighted Avg Recall | Weighted Avg F1-score | Weighted Avg Support | |
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|:-------------:|:-----:|:-----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:| |
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| 0.2065 | 1.0 | 2708 | 0.1948 | 0.9182 | 0.8800 | 0.8987 | 7717.0 | 0.9012 | 0.8923 | 0.8967 | 7717.0 | 0.7478 | 0.8576 | 0.7990 | 3244.0 | 0.7788 | 0.7322 | 0.7548 | 1322.0 | 0.8713 | 0.8365 | 0.8405 | 0.8373 | 20000.0 | 0.8748 | 0.8713 | 0.8722 | 20000.0 | |
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| 0.1833 | 2.0 | 5416 | 0.1898 | 0.9121 | 0.9051 | 0.9086 | 7717.0 | 0.9113 | 0.9016 | 0.9065 | 7717.0 | 0.7992 | 0.8098 | 0.8045 | 3244.0 | 0.7401 | 0.7950 | 0.7666 | 1322.0 | 0.8810 | 0.8407 | 0.8529 | 0.8465 | 20000.0 | 0.8821 | 0.8810 | 0.8815 | 20000.0 | |
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| 0.1415 | 3.0 | 8124 | 0.2006 | 0.8913 | 0.9220 | 0.9064 | 7717.0 | 0.9039 | 0.9116 | 0.9077 | 7717.0 | 0.8096 | 0.7747 | 0.7917 | 3244.0 | 0.8018 | 0.6853 | 0.7390 | 1322.0 | 0.8784 | 0.8516 | 0.8234 | 0.8362 | 20000.0 | 0.8770 | 0.8784 | 0.8772 | 20000.0 | |
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| 0.1136 | 4.0 | 10832 | 0.2063 | 0.9045 | 0.9136 | 0.9090 | 7717.0 | 0.9038 | 0.9106 | 0.9072 | 7717.0 | 0.7968 | 0.8039 | 0.8004 | 3244.0 | 0.7876 | 0.6899 | 0.7355 | 1322.0 | 0.8799 | 0.8482 | 0.8295 | 0.8380 | 20000.0 | 0.8790 | 0.8799 | 0.8792 | 20000.0 | |
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| 0.1051 | 5.0 | 13540 | 0.2285 | 0.9131 | 0.9079 | 0.9105 | 7717.0 | 0.9138 | 0.9093 | 0.9115 | 7717.0 | 0.7882 | 0.7975 | 0.7928 | 3244.0 | 0.7313 | 0.7557 | 0.7433 | 1322.0 | 0.8804 | 0.8366 | 0.8426 | 0.8395 | 20000.0 | 0.8811 | 0.8804 | 0.8807 | 20000.0 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.0.1 |
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- Tokenizers 0.21.0 |
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