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README.md
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---
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base_model: gokuls/HBERTv1_48_L10_H512_A8
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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: HBERTv1_48_L10_H512_A8_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: 0.8583374323659616
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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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# HBERTv1_48_L10_H512_A8_massive
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This model is a fine-tuned version of [gokuls/HBERTv1_48_L10_H512_A8](https://huggingface.co/gokuls/HBERTv1_48_L10_H512_A8) on the massive dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8593
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- Accuracy: 0.8583
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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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| 2.6536 | 1.0 | 180 | 1.3212 | 0.6542 |
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| 1.0696 | 2.0 | 360 | 0.8384 | 0.7777 |
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| 0.701 | 3.0 | 540 | 0.7514 | 0.8082 |
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| 0.5181 | 4.0 | 720 | 0.7376 | 0.8096 |
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| 0.3958 | 5.0 | 900 | 0.6764 | 0.8269 |
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| 0.3031 | 6.0 | 1080 | 0.6955 | 0.8382 |
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| 0.2378 | 7.0 | 1260 | 0.7173 | 0.8392 |
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| 0.1719 | 8.0 | 1440 | 0.7289 | 0.8401 |
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| 0.1294 | 9.0 | 1620 | 0.7609 | 0.8485 |
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| 0.096 | 10.0 | 1800 | 0.7744 | 0.8465 |
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| 0.0769 | 11.0 | 1980 | 0.8206 | 0.8490 |
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| 0.05 | 12.0 | 2160 | 0.8085 | 0.8564 |
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| 0.034 | 13.0 | 2340 | 0.8537 | 0.8534 |
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| 0.0218 | 14.0 | 2520 | 0.8480 | 0.8564 |
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| 0.0139 | 15.0 | 2700 | 0.8593 | 0.8583 |
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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 0.14.0
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