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End of training

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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: somukandula/maskara-tiny
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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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+ - f1
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+ model-index:
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+ - name: maskara-tiny-v2
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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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+ # maskara-tiny-v2
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+
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+ This model is a fine-tuned version of [somukandula/maskara-tiny](https://huggingface.co/somukandula/maskara-tiny) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.0869
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+ - Precision: 0.0225
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+ - Recall: 0.4320
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+ - F1: 0.0428
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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: 8e-06
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+ - train_batch_size: 32
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+ - eval_batch_size: 64
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+ - seed: 42
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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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+ - num_epochs: 2
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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 |
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+ |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|
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+ | 1.2055 | 0.0936 | 1000 | 1.4326 | 0.0151 | 0.3009 | 0.0288 |
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+ | 1.0776 | 0.1873 | 2000 | 1.2621 | 0.0191 | 0.3655 | 0.0364 |
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+ | 0.9771 | 0.2809 | 3000 | 1.1981 | 0.0211 | 0.4012 | 0.0400 |
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+ | 0.9773 | 0.3745 | 4000 | 1.1648 | 0.0215 | 0.4185 | 0.0408 |
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+ | 0.942 | 0.4682 | 5000 | 1.1355 | 0.0228 | 0.4359 | 0.0434 |
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+ | 0.9314 | 0.5618 | 6000 | 1.1103 | 0.0230 | 0.4359 | 0.0437 |
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+ | 0.9201 | 0.6554 | 7000 | 1.1048 | 0.0221 | 0.4301 | 0.0420 |
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+ | 0.9042 | 0.7491 | 8000 | 1.0869 | 0.0225 | 0.4320 | 0.0428 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.2
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
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