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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- arxiv_dataset |
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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: baseline_BERT_10K_steps |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: arxiv_dataset |
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type: arxiv_dataset |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9905994544286106 |
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- name: Precision |
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type: precision |
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value: 0.7827298050139275 |
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- name: Recall |
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type: recall |
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value: 0.05172572480441785 |
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- name: F1 |
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type: f1 |
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value: 0.09703876370543037 |
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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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# baseline_BERT_10K_steps |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the arxiv_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0356 |
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- Accuracy: 0.9906 |
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- Precision: 0.7827 |
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- Recall: 0.0517 |
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- F1: 0.0970 |
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- Hamming: 0.0094 |
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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: 6 |
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- eval_batch_size: 6 |
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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: 0.1 |
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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Hamming | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| |
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| No log | 0.0 | 500 | 0.1602 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 | |
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| No log | 0.0 | 1000 | 0.0573 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 | |
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| No log | 0.0 | 1500 | 0.0504 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 | |
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| No log | 0.01 | 2000 | 0.0492 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 | |
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| No log | 0.01 | 2500 | 0.0488 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 | |
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| No log | 0.01 | 3000 | 0.0485 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 | |
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| No log | 0.01 | 3500 | 0.0477 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 | |
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| No log | 0.01 | 4000 | 0.0467 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 | |
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| No log | 0.01 | 4500 | 0.0455 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 | |
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| No log | 0.01 | 5000 | 0.0442 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 | |
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| No log | 0.02 | 5500 | 0.0422 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 | |
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| No log | 0.02 | 6000 | 0.0408 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 | |
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| No log | 0.02 | 6500 | 0.0394 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 | |
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| No log | 0.02 | 7000 | 0.0385 | 0.9902 | 1.0 | 0.0011 | 0.0022 | 0.0098 | |
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| No log | 0.02 | 7500 | 0.0376 | 0.9903 | 0.7949 | 0.0057 | 0.0113 | 0.0097 | |
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| No log | 0.02 | 8000 | 0.0368 | 0.9903 | 0.8071 | 0.0146 | 0.0287 | 0.0097 | |
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| No log | 0.03 | 8500 | 0.0363 | 0.9905 | 0.7372 | 0.0465 | 0.0874 | 0.0095 | |
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| No log | 0.03 | 9000 | 0.0359 | 0.9905 | 0.7811 | 0.0381 | 0.0727 | 0.0095 | |
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| No log | 0.03 | 9500 | 0.0357 | 0.9906 | 0.8029 | 0.0562 | 0.1051 | 0.0094 | |
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| 0.0665 | 0.03 | 10000 | 0.0356 | 0.9906 | 0.7827 | 0.0517 | 0.0970 | 0.0094 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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