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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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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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base_model: distilbert-base-uncased |
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model-index: |
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- name: distilbert-amazon-shoe-reviews-tensorboard |
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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-amazon-shoe-reviews-tensorboard |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9534 |
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- Accuracy: 0.5779 |
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- F1: [0.63189419 0.46645049 0.50381304 0.55843496 0.73060507] |
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- Precision: [0.62953754 0.47008547 0.48669202 0.58801498 0.71780957] |
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- Recall: [0.63426854 0.46287129 0.52218256 0.53168844 0.74386503] |
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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: 32 |
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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: 1 |
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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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| 0.8776 | 1.0 | 2813 | 0.9534 | 0.5779 | [0.63189419 0.46645049 0.50381304 0.55843496 0.73060507] | [0.62953754 0.47008547 0.48669202 0.58801498 0.71780957] | [0.63426854 0.46287129 0.52218256 0.53168844 0.74386503] | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0+cu102 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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