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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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metrics: |
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- accuracy |
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model-index: |
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- name: distilbert-base-uncased-lora-text-classification |
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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-lora-text-classification |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a truncated IMDB dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7208 |
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- Accuracy: {'accuracy': 0.876} |
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## Model description |
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The purpose of this model is to turn distilbert into a sentiment classification model. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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: 4 |
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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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| No log | 1.0 | 250 | 2.0890 | {'accuracy': 0.862} | |
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| 0.2005 | 2.0 | 500 | 1.8919 | {'accuracy': 0.874} | |
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| 0.2005 | 3.0 | 750 | 1.7205 | {'accuracy': 0.871} | |
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| 0.0963 | 4.0 | 1000 | 1.7208 | {'accuracy': 0.876} | |
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### Framework versions |
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- Transformers 4.37.1 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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