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--- |
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license: apache-2.0 |
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base_model: distilbert/distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: distilbert_base_uncased_twitter |
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results: [] |
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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_twitter |
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4864 |
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- Accuracy: 0.7665 |
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- F1 Macro: 0.7130 |
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- F1 Micro: 0.7665 |
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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: 2e-05 |
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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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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 64 |
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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: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:| |
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| 0.4671 | 0.37 | 50 | 0.4990 | 0.7665 | 0.7214 | 0.7665 | |
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| 0.4724 | 0.74 | 100 | 0.4864 | 0.7665 | 0.7130 | 0.7665 | |
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| 0.4569 | 1.1 | 150 | 0.4924 | 0.7619 | 0.7171 | 0.7619 | |
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| 0.4577 | 1.47 | 200 | 0.4881 | 0.7675 | 0.7357 | 0.7675 | |
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| 0.4438 | 1.84 | 250 | 0.4902 | 0.7638 | 0.7222 | 0.7638 | |
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| 0.405 | 2.21 | 300 | 0.4901 | 0.7647 | 0.7211 | 0.7647 | |
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| 0.4308 | 2.57 | 350 | 0.4900 | 0.7693 | 0.7326 | 0.7693 | |
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| 0.3584 | 2.94 | 400 | 0.4931 | 0.7675 | 0.7288 | 0.7675 | |
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### Framework versions |
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- Transformers 4.39.0.dev0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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