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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: albert/albert-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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- precision |
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- recall |
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model-index: |
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- name: classify-articles |
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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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# classify-articles |
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This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co./albert/albert-base-v2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3819 |
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- Accuracy: 0.9070 |
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- F1: 0.9061 |
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- Precision: 0.9126 |
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- Recall: 0.9070 |
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- Accuracy Label Economy: 0.9429 |
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- Accuracy Label Politics: 0.9574 |
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- Accuracy Label Science: 0.9362 |
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- Accuracy Label Sports: 0.96 |
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- Accuracy Label Technology: 0.6944 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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_steps: 500 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Accuracy Label Economy | Accuracy Label Politics | Accuracy Label Science | Accuracy Label Sports | Accuracy Label Technology | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:----------------------:|:-----------------------:|:----------------------:|:---------------------:|:-------------------------:| |
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| 1.3703 | 1.3072 | 100 | 1.3775 | 0.4930 | 0.4238 | 0.6100 | 0.4930 | 0.8 | 0.0213 | 0.7021 | 0.72 | 0.2222 | |
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| 0.4329 | 2.6144 | 200 | 0.4495 | 0.8977 | 0.9004 | 0.9134 | 0.8977 | 0.9429 | 0.8936 | 0.9149 | 0.96 | 0.75 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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