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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- dstefa/New_York_Times_Topics |
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metrics: |
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- accuracy |
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model-index: |
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- name: DistilBERT base classify news topics - Devinit |
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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: New York Times Topics |
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type: dstefa/New_York_Times_Topics |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.913482481060606 |
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widget: |
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- text: "Insurers: Costs Would Skyrocket Under House Health Bill." |
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--- |
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# DistilBERT base classify news topics - Devinit |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the New York Times Topics dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2871 |
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- Accuracy: 0.9135 |
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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: 128 |
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- eval_batch_size: 128 |
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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: 10 |
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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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| 0.386 | 1.0 | 1340 | 0.3275 | 0.8921 | |
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| 0.2833 | 2.0 | 2680 | 0.2840 | 0.9033 | |
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| 0.2411 | 3.0 | 4020 | 0.2694 | 0.9102 | |
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| 0.2069 | 4.0 | 5360 | 0.2665 | 0.9114 | |
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| 0.1796 | 5.0 | 6700 | 0.2657 | 0.9128 | |
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| 0.1636 | 6.0 | 8040 | 0.2674 | 0.9142 | |
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| 0.144 | 7.0 | 9380 | 0.2761 | 0.9129 | |
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| 0.1277 | 8.0 | 10720 | 0.2820 | 0.9125 | |
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| 0.1201 | 9.0 | 12060 | 0.2853 | 0.9136 | |
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| 0.1104 | 10.0 | 13400 | 0.2871 | 0.9135 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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