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README.md ADDED
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+ ---
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+ base_model: vinai/phobert-base-v2
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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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+ - f1
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+ model-index:
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+ - name: PhoBert_Lexical_Dataset55K
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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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+ # PhoBert_Lexical_Dataset55K
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+
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+ This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.0598
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+ - Accuracy: 0.7970
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+ - F1: 0.8468
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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: 64
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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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+ - num_epochs: 15
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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 | F1 |
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+ |:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|
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+ | No log | 0.2317 | 200 | 0.5915 | 0.7575 | 0.8196 |
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+ | No log | 0.4635 | 400 | 0.6149 | 0.7294 | 0.8009 |
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+ | No log | 0.6952 | 600 | 0.5838 | 0.7335 | 0.8038 |
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+ | No log | 0.9270 | 800 | 0.4301 | 0.8213 | 0.8606 |
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+ | 0.3511 | 1.1587 | 1000 | 0.4820 | 0.8211 | 0.8608 |
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+ | 0.3511 | 1.3905 | 1200 | 0.6798 | 0.7461 | 0.8126 |
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+ | 0.3511 | 1.6222 | 1400 | 0.6930 | 0.7087 | 0.7868 |
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+ | 0.3511 | 1.8540 | 1600 | 0.6207 | 0.7709 | 0.8292 |
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+ | 0.2551 | 2.0857 | 1800 | 0.5473 | 0.8101 | 0.8545 |
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+ | 0.2551 | 2.3175 | 2000 | 0.5228 | 0.8072 | 0.8526 |
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+ | 0.2551 | 2.5492 | 2200 | 0.5701 | 0.7828 | 0.8371 |
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+ | 0.2551 | 2.7810 | 2400 | 0.5560 | 0.7838 | 0.8378 |
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+ | 0.214 | 3.0127 | 2600 | 0.7375 | 0.7332 | 0.8039 |
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+ | 0.214 | 3.2445 | 2800 | 0.5101 | 0.8126 | 0.8565 |
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+ | 0.214 | 3.4762 | 3000 | 0.5639 | 0.8139 | 0.8574 |
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+ | 0.214 | 3.7080 | 3200 | 0.4802 | 0.8299 | 0.8671 |
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+ | 0.214 | 3.9397 | 3400 | 0.6431 | 0.7773 | 0.8336 |
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+ | 0.1835 | 4.1715 | 3600 | 0.8122 | 0.7347 | 0.8051 |
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+ | 0.1835 | 4.4032 | 3800 | 0.4787 | 0.8506 | 0.8804 |
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+ | 0.1835 | 4.6350 | 4000 | 0.4719 | 0.8476 | 0.8784 |
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+ | 0.1835 | 4.8667 | 4200 | 0.4643 | 0.8419 | 0.8751 |
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+ | 0.157 | 5.0985 | 4400 | 0.6827 | 0.7918 | 0.8432 |
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+ | 0.157 | 5.3302 | 4600 | 0.5695 | 0.8225 | 0.8629 |
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+ | 0.157 | 5.5620 | 4800 | 0.7416 | 0.7831 | 0.8376 |
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+ | 0.157 | 5.7937 | 5000 | 0.7787 | 0.7643 | 0.8253 |
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+ | 0.1337 | 6.0255 | 5200 | 0.6881 | 0.7963 | 0.8462 |
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+ | 0.1337 | 6.2572 | 5400 | 0.6894 | 0.7953 | 0.8456 |
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+ | 0.1337 | 6.4890 | 5600 | 0.6472 | 0.8156 | 0.8587 |
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+ | 0.1337 | 6.7207 | 5800 | 0.5857 | 0.8222 | 0.8626 |
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+ | 0.1337 | 6.9525 | 6000 | 0.6816 | 0.8113 | 0.8559 |
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+ | 0.1154 | 7.1842 | 6200 | 0.9047 | 0.7590 | 0.8216 |
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+ | 0.1154 | 7.4160 | 6400 | 0.7080 | 0.8088 | 0.8541 |
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+ | 0.1154 | 7.6477 | 6600 | 0.6871 | 0.8148 | 0.8581 |
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+ | 0.1154 | 7.8795 | 6800 | 0.7481 | 0.7900 | 0.8422 |
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+ | 0.1002 | 8.1112 | 7000 | 0.7101 | 0.8236 | 0.8637 |
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+ | 0.1002 | 8.3430 | 7200 | 0.8398 | 0.7933 | 0.8443 |
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+ | 0.1002 | 8.5747 | 7400 | 0.7284 | 0.8239 | 0.8640 |
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+ | 0.1002 | 8.8065 | 7600 | 0.7415 | 0.8117 | 0.8561 |
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+ | 0.0843 | 9.0382 | 7800 | 0.8033 | 0.8066 | 0.8529 |
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+ | 0.0843 | 9.2700 | 8000 | 0.8593 | 0.8080 | 0.8536 |
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+ | 0.0843 | 9.5017 | 8200 | 0.8626 | 0.7953 | 0.8456 |
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+ | 0.0843 | 9.7335 | 8400 | 0.7607 | 0.8192 | 0.8609 |
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+ | 0.0843 | 9.9652 | 8600 | 0.9512 | 0.7875 | 0.8406 |
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+ | 0.072 | 10.1970 | 8800 | 0.9709 | 0.7891 | 0.8418 |
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+ | 0.072 | 10.4287 | 9000 | 0.9674 | 0.7948 | 0.8453 |
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+ | 0.072 | 10.6605 | 9200 | 0.9815 | 0.7863 | 0.8399 |
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+ | 0.072 | 10.8922 | 9400 | 1.0347 | 0.7839 | 0.8382 |
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+ | 0.0617 | 11.1240 | 9600 | 1.1195 | 0.7774 | 0.8340 |
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+ | 0.0617 | 11.3557 | 9800 | 1.0192 | 0.7916 | 0.8434 |
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+ | 0.0617 | 11.5875 | 10000 | 0.9594 | 0.8022 | 0.8502 |
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+ | 0.0617 | 11.8192 | 10200 | 1.0892 | 0.7750 | 0.8325 |
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+ | 0.0541 | 12.0510 | 10400 | 1.0634 | 0.7886 | 0.8414 |
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+ | 0.0541 | 12.2827 | 10600 | 1.0198 | 0.8036 | 0.8512 |
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+ | 0.0541 | 12.5145 | 10800 | 1.0040 | 0.8016 | 0.8499 |
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+ | 0.0541 | 12.7462 | 11000 | 1.0208 | 0.7962 | 0.8464 |
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+ | 0.0541 | 12.9780 | 11200 | 1.0329 | 0.7927 | 0.8441 |
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+ | 0.048 | 13.2097 | 11400 | 0.9916 | 0.8033 | 0.8510 |
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+ | 0.048 | 13.4415 | 11600 | 1.0793 | 0.7902 | 0.8424 |
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+ | 0.048 | 13.6732 | 11800 | 1.0424 | 0.7964 | 0.8465 |
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+ | 0.048 | 13.9050 | 12000 | 1.0162 | 0.8018 | 0.8500 |
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+ | 0.0426 | 14.1367 | 12200 | 1.0633 | 0.7936 | 0.8447 |
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+ | 0.0426 | 14.3685 | 12400 | 1.0128 | 0.8022 | 0.8502 |
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+ | 0.0426 | 14.6002 | 12600 | 1.0780 | 0.7924 | 0.8439 |
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+ | 0.0426 | 14.8320 | 12800 | 1.0598 | 0.7970 | 0.8468 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.0
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+ - Pytorch 2.1.2
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
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