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tuned-albert-tiny

Text Classification
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albert
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  1. README.md +81 -0
  2. model.safetensors +1 -1
  3. runs/Apr16_17-08-14_pop-os/events.out.tfevents.1744794494.pop-os.4807.0 +3 -0
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  5. runs/Apr16_17-23-00_pop-os/events.out.tfevents.1744795380.pop-os.5079.0 +3 -0
  6. runs/Apr16_17-23-00_pop-os/events.out.tfevents.1744795385.pop-os.5079.1 +3 -0
  7. training_args.bin +1 -1
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: gpl-3.0
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+ base_model: ckiplab/albert-tiny-chinese
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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: tuned-albert-tiny
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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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+ # tuned-albert-tiny
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+
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+ This model is a fine-tuned version of [ckiplab/albert-tiny-chinese](https://huggingface.co/ckiplab/albert-tiny-chinese) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6326
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+ - Accuracy: 1.0
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+ - Precision: 1.0
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+ - Recall: 1.0
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+ - F1: 1.0
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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: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - optimizer: Use 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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+ - num_epochs: 3
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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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.6878 | 0.2 | 1 | 0.6827 | 0.625 | 0.75 | 0.7 | 0.6190 |
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+ | 0.6849 | 0.4 | 2 | 0.6786 | 0.625 | 0.75 | 0.7 | 0.6190 |
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+ | 0.6747 | 0.6 | 3 | 0.6729 | 0.875 | 0.875 | 0.9 | 0.8730 |
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+ | 0.6763 | 0.8 | 4 | 0.6672 | 0.9375 | 0.9286 | 0.95 | 0.9352 |
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+ | 0.6634 | 1.0 | 5 | 0.6628 | 0.9375 | 0.9286 | 0.95 | 0.9352 |
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+ | 0.6562 | 1.2 | 6 | 0.6589 | 0.9375 | 0.9286 | 0.95 | 0.9352 |
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+ | 0.6516 | 1.4 | 7 | 0.6553 | 0.9375 | 0.9286 | 0.95 | 0.9352 |
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+ | 0.6525 | 1.6 | 8 | 0.6517 | 0.9375 | 0.9286 | 0.95 | 0.9352 |
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+ | 0.6497 | 1.8 | 9 | 0.6477 | 0.9375 | 0.9286 | 0.95 | 0.9352 |
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+ | 0.6432 | 2.0 | 10 | 0.6439 | 0.9375 | 0.9286 | 0.95 | 0.9352 |
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+ | 0.6488 | 2.2 | 11 | 0.6403 | 0.9375 | 0.9286 | 0.95 | 0.9352 |
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+ | 0.6384 | 2.4 | 12 | 0.6373 | 0.9375 | 0.9286 | 0.95 | 0.9352 |
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+ | 0.6236 | 2.6 | 13 | 0.6349 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.6254 | 2.8 | 14 | 0.6334 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.6346 | 3.0 | 15 | 0.6326 | 1.0 | 1.0 | 1.0 | 1.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.49.0
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+ - Pytorch 2.5.1+cu124
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+ - Datasets 3.5.0
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+ - Tokenizers 0.21.0
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