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--- |
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license: apache-2.0 |
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base_model: bert-large-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- gokuls/wiki_book_corpus_complete_processed_bert_dataset |
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metrics: |
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- accuracy |
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model-index: |
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- name: BERT_pretraining_h_100_wo_deepspeed |
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results: |
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- task: |
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name: Masked Language Modeling |
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type: fill-mask |
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dataset: |
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name: gokuls/wiki_book_corpus_complete_processed_bert_dataset |
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type: gokuls/wiki_book_corpus_complete_processed_bert_dataset |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.15387755648267093 |
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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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# BERT_pretraining_h_100_wo_deepspeed |
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This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 5.7778 |
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- Accuracy: 0.1539 |
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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: 1e-05 |
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- train_batch_size: 208 |
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- eval_batch_size: 208 |
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- seed: 10 |
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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: 100000 |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:| |
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| 6.8769 | 0.36 | 10000 | 6.7582 | 0.1101 | |
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| 6.4647 | 0.71 | 20000 | 6.4764 | 0.1314 | |
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| 6.3679 | 1.07 | 30000 | 6.3218 | 0.1407 | |
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| 6.252 | 1.42 | 40000 | 6.2139 | 0.1454 | |
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| 6.2132 | 1.78 | 50000 | 6.1398 | 0.1478 | |
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| 6.0407 | 2.13 | 60000 | 6.0774 | 0.1502 | |
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| 6.0694 | 2.49 | 70000 | 6.0303 | 0.1516 | |
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| 5.9996 | 2.84 | 80000 | 5.9893 | 0.1521 | |
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| 5.9166 | 3.2 | 90000 | 5.9553 | 0.1526 | |
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| 5.8915 | 3.55 | 100000 | 5.9261 | 0.1530 | |
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| 5.8924 | 3.91 | 110000 | 5.8996 | 0.1534 | |
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| 5.8972 | 4.26 | 120000 | 5.8814 | 0.1533 | |
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| 5.8454 | 4.62 | 130000 | 5.8626 | 0.1532 | |
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| 5.8104 | 4.97 | 140000 | 5.8494 | 0.1534 | |
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| 5.8461 | 5.33 | 150000 | 5.8378 | 0.1534 | |
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| 5.8476 | 5.68 | 160000 | 5.8246 | 0.1536 | |
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| 5.7255 | 6.04 | 170000 | 5.8155 | 0.1532 | |
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| 5.8431 | 6.39 | 180000 | 5.8068 | 0.1537 | |
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| 5.7526 | 6.75 | 190000 | 5.7981 | 0.1537 | |
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| 5.7826 | 7.1 | 200000 | 5.7886 | 0.1537 | |
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### Framework versions |
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- Transformers 4.37.1 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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