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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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datasets: |
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- autextification2023 |
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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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model-index: |
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- name: ia-detection-distilbert-base-cased |
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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: autextification2023 |
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type: autextification2023 |
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config: detection_en |
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split: train |
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args: detection_en |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.6757969952363503 |
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- name: F1 |
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type: f1 |
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value: 0.7481855699444998 |
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- name: Precision |
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type: precision |
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value: 0.6215273673010995 |
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- name: Recall |
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type: recall |
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value: 0.9396782841823056 |
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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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# ia-detection-distilbert-base-cased |
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the autextification2023 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1147 |
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- Accuracy: 0.6758 |
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- F1: 0.7482 |
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- Precision: 0.6215 |
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- Recall: 0.9397 |
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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: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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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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| 0.4298 | 1.0 | 3808 | 0.5010 | 0.7725 | 0.8114 | 0.6964 | 0.9718 | |
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| 0.4464 | 2.0 | 7616 | 0.4737 | 0.8514 | 0.8531 | 0.8493 | 0.8568 | |
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| 0.4296 | 3.0 | 11424 | 0.4870 | 0.8402 | 0.8424 | 0.8363 | 0.8486 | |
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| 0.2034 | 4.0 | 15232 | 0.5404 | 0.8493 | 0.8510 | 0.8475 | 0.8545 | |
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| 0.0803 | 5.0 | 19040 | 0.6954 | 0.8520 | 0.8491 | 0.8724 | 0.8269 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.13.3 |
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