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README.md
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---
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license: apache-2.0
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base_model: google/bert_uncased_L-4_H-256_A-4
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tags:
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- generated_from_trainer
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datasets:
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- massive
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metrics:
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- accuracy
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model-index:
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- name: bert_uncased_L-4_H-256_A-4_massive
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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: massive
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type: massive
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config: en-US
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split: validation
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args: en-US
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8362026561731432
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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_uncased_L-4_H-256_A-4_massive
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This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the massive dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7252
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- Accuracy: 0.8362
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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: 64
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- eval_batch_size: 64
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- seed: 33
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- distributed_type: multi-GPU
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 3.5031 | 1.0 | 180 | 2.8542 | 0.4437 |
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| 2.5403 | 2.0 | 360 | 2.0782 | 0.6394 |
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| 1.928 | 3.0 | 540 | 1.6213 | 0.7118 |
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| 1.542 | 4.0 | 720 | 1.3355 | 0.7526 |
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| 1.2771 | 5.0 | 900 | 1.1556 | 0.7801 |
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| 1.0852 | 6.0 | 1080 | 1.0223 | 0.7964 |
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| 0.939 | 7.0 | 1260 | 0.9331 | 0.8047 |
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| 0.8352 | 8.0 | 1440 | 0.8670 | 0.8146 |
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| 0.7522 | 9.0 | 1620 | 0.8184 | 0.8190 |
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| 0.6847 | 10.0 | 1800 | 0.7887 | 0.8254 |
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| 0.6369 | 11.0 | 1980 | 0.7578 | 0.8254 |
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| 0.5943 | 12.0 | 2160 | 0.7413 | 0.8323 |
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| 0.5652 | 13.0 | 2340 | 0.7288 | 0.8328 |
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| 0.5486 | 14.0 | 2520 | 0.7252 | 0.8362 |
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| 0.5394 | 15.0 | 2700 | 0.7190 | 0.8357 |
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### Framework versions
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- Transformers 4.34.0
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- Pytorch 1.14.0a0+410ce96
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- Datasets 2.14.5
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- Tokenizers 0.14.1
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