update model card README.md
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README.md
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---
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license: mit
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tags:
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- generated_from_trainer
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datasets:
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- ag_news
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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: results
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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: ag_news
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type: ag_news
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config: default
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split: train[:40000]
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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: 0.8951
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- name: F1
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type: f1
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value: 0.8964447542636089
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- name: Precision
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type: precision
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value: 0.8978261707981314
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- name: Recall
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type: recall
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value: 0.896474840596734
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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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# results
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This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the ag_news dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3320
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- Accuracy: 0.8951
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- F1: 0.8964
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- Precision: 0.8978
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- Recall: 0.8965
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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: 0.0003
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- train_batch_size: 64
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- eval_batch_size: 64
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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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- num_epochs: 3
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| 0.2783 | 1.0 | 625 | 0.3046 | 0.8949 | 0.8960 | 0.8970 | 0.8963 |
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| 0.1878 | 2.0 | 1250 | 0.3139 | 0.8954 | 0.8971 | 0.8995 | 0.8965 |
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| 0.1311 | 3.0 | 1875 | 0.3320 | 0.8951 | 0.8964 | 0.8978 | 0.8965 |
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### Framework versions
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- Transformers 4.22.0
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- Pytorch 1.12.1+cu113
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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