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--- |
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license: apache-2.0 |
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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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- precision |
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- recall |
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base_model: salohnana2018/CAMEL-BERT-MSA-domianAdaption-Single-ABSA-HARD |
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model-index: |
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- name: ABSA-SentencePair-DAPT-HARDARABS-bert-base-Camel-MSA-ru1 |
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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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# ABSA-SentencePair-DAPT-HARDARABS-bert-base-Camel-MSA-ru1 |
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This model is a fine-tuned version of [salohnana2018/CAMEL-BERT-MSA-domianAdaption-Single-ABSA-HARD](https://huggingface.co./salohnana2018/CAMEL-BERT-MSA-domianAdaption-Single-ABSA-HARD) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6590 |
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- Accuracy: 0.8965 |
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- F1: 0.8965 |
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- Precision: 0.8965 |
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- Recall: 0.8965 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 25 |
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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: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.5505 | 1.0 | 265 | 0.4846 | 0.7703 | 0.7703 | 0.7703 | 0.7703 | |
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| 0.354 | 2.0 | 530 | 0.3727 | 0.8852 | 0.8852 | 0.8852 | 0.8852 | |
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| 0.2455 | 3.0 | 795 | 0.4606 | 0.8582 | 0.8582 | 0.8582 | 0.8582 | |
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| 0.179 | 4.0 | 1060 | 0.5599 | 0.8771 | 0.8771 | 0.8771 | 0.8771 | |
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| 0.1452 | 5.0 | 1325 | 0.8984 | 0.8795 | 0.8795 | 0.8795 | 0.8795 | |
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| 0.1076 | 6.0 | 1590 | 0.8935 | 0.8960 | 0.8960 | 0.8960 | 0.8960 | |
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| 0.093 | 7.0 | 1855 | 0.6251 | 0.8757 | 0.8757 | 0.8757 | 0.8757 | |
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| 0.0779 | 8.0 | 2120 | 0.9415 | 0.8899 | 0.8899 | 0.8899 | 0.8899 | |
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| 0.0653 | 9.0 | 2385 | 0.9360 | 0.8965 | 0.8965 | 0.8965 | 0.8965 | |
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| 0.0514 | 10.0 | 2650 | 1.0234 | 0.8889 | 0.8889 | 0.8889 | 0.8889 | |
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| 0.0367 | 11.0 | 2915 | 1.3198 | 0.8951 | 0.8951 | 0.8951 | 0.8951 | |
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| 0.0414 | 12.0 | 3180 | 1.1830 | 0.8833 | 0.8833 | 0.8833 | 0.8833 | |
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| 0.0396 | 13.0 | 3445 | 1.1262 | 0.8885 | 0.8885 | 0.8885 | 0.8885 | |
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| 0.0338 | 14.0 | 3710 | 1.3073 | 0.8970 | 0.8970 | 0.8970 | 0.8970 | |
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| 0.0204 | 15.0 | 3975 | 1.2894 | 0.8946 | 0.8946 | 0.8946 | 0.8946 | |
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| 0.0232 | 16.0 | 4240 | 1.3265 | 0.8960 | 0.8960 | 0.8960 | 0.8960 | |
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| 0.0203 | 17.0 | 4505 | 1.2467 | 0.8974 | 0.8974 | 0.8974 | 0.8974 | |
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| 0.012 | 18.0 | 4770 | 1.5870 | 0.8960 | 0.8960 | 0.8960 | 0.8960 | |
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| 0.0155 | 19.0 | 5035 | 1.5296 | 0.8974 | 0.8974 | 0.8974 | 0.8974 | |
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| 0.0112 | 20.0 | 5300 | 1.6052 | 0.9026 | 0.9026 | 0.9026 | 0.9026 | |
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| 0.0109 | 21.0 | 5565 | 1.5280 | 0.8974 | 0.8974 | 0.8974 | 0.8974 | |
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| 0.012 | 22.0 | 5830 | 1.5513 | 0.8941 | 0.8941 | 0.8941 | 0.8941 | |
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| 0.0103 | 23.0 | 6095 | 1.6142 | 0.8984 | 0.8984 | 0.8984 | 0.8984 | |
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| 0.0116 | 24.0 | 6360 | 1.5363 | 0.8965 | 0.8965 | 0.8965 | 0.8965 | |
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| 0.0104 | 25.0 | 6625 | 1.6729 | 0.8984 | 0.8984 | 0.8984 | 0.8984 | |
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| 0.0079 | 26.0 | 6890 | 1.7198 | 0.8993 | 0.8993 | 0.8993 | 0.8993 | |
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| 0.0093 | 27.0 | 7155 | 1.5210 | 0.8941 | 0.8941 | 0.8941 | 0.8941 | |
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| 0.0077 | 28.0 | 7420 | 1.6023 | 0.8960 | 0.8960 | 0.8960 | 0.8960 | |
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| 0.0077 | 29.0 | 7685 | 1.6430 | 0.8960 | 0.8960 | 0.8960 | 0.8960 | |
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| 0.0073 | 30.0 | 7950 | 1.6590 | 0.8965 | 0.8965 | 0.8965 | 0.8965 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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