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--- |
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license: apache-2.0 |
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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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base_model: distilbert-base-uncased |
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model-index: |
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- name: distilbert-base-uncased-finetuned-news |
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results: |
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- task: |
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type: text-classification |
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name: 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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args: default |
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metrics: |
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- type: accuracy |
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value: 0.9388157894736842 |
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name: Accuracy |
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- type: f1 |
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value: 0.9388275184627893 |
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name: F1 |
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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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# distilbert-base-uncased-finetuned-news |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2117 |
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- Accuracy: 0.9388 |
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- F1: 0.9388 |
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## Model description |
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This model is intended to categorize news headlines into one of four categories; World, Sports, Science & Technology, or Business |
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## Intended uses & limitations |
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The model is limited by the training data it used. If you use the model with a news story that falls outside of the four intended categories, it produces quite confused results. |
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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: 0.0002 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.2949 | 1.0 | 3750 | 0.2501 | 0.9262 | 0.9261 | |
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| 0.1569 | 2.0 | 7500 | 0.2117 | 0.9388 | 0.9388 | |
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### Framework versions |
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- Transformers 4.20.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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