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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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- essay_dataset |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: B001_cleaned |
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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: essay_dataset |
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type: essay_dataset |
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config: cleaned |
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split: test |
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args: cleaned |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: |
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accuracy: 0.10526315789473684 |
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- name: Precision |
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type: precision |
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value: |
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precision: 0.013157894736842105 |
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- name: Recall |
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type: recall |
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value: |
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recall: 0.125 |
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- name: F1 |
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type: f1 |
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value: |
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f1: 0.02380952380952381 |
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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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# B001_cleaned |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the essay_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.2117 |
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- Accuracy: {'accuracy': 0.10526315789473684} |
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- Precision: {'precision': 0.013157894736842105} |
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- Recall: {'recall': 0.125} |
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- F1: {'f1': 0.02380952380952381} |
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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: 16 |
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- eval_batch_size: 16 |
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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 | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:-----------------------------------:|:-----------------:|:---------------------------:| |
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| No log | 1.0 | 13 | 2.2061 | {'accuracy': 0.10526315789473684} | {'precision': 0.013157894736842105} | {'recall': 0.125} | {'f1': 0.02380952380952381} | |
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| No log | 2.0 | 26 | 2.2050 | {'accuracy': 0.10526315789473684} | {'precision': 0.013157894736842105} | {'recall': 0.125} | {'f1': 0.02380952380952381} | |
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| No log | 3.0 | 39 | 2.2045 | {'accuracy': 0.10526315789473684} | {'precision': 0.013157894736842105} | {'recall': 0.125} | {'f1': 0.02380952380952381} | |
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| No log | 4.0 | 52 | 2.2117 | {'accuracy': 0.10526315789473684} | {'precision': 0.013157894736842105} | {'recall': 0.125} | {'f1': 0.02380952380952381} | |
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### Framework versions |
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- Transformers 4.37.1 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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