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README.md
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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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- eoir_privacy
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metrics:
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- accuracy
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- f1
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model-index:
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- name: distilbert-base-uncased-finetuned-eoir_privacy
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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: eoir_privacy
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type: eoir_privacy
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args: all
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9052835051546392
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- name: F1
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type: f1
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value: 0.8088426527958388
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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-finetuned-eoir_privacy
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the eoir_privacy dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3681
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- Accuracy: 0.9053
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- F1: 0.8088
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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: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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| No log | 1.0 | 395 | 0.3053 | 0.8789 | 0.7432 |
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| 0.3562 | 2.0 | 790 | 0.2857 | 0.8976 | 0.7883 |
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| 0.2217 | 3.0 | 1185 | 0.3358 | 0.8905 | 0.7550 |
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| 0.1509 | 4.0 | 1580 | 0.3505 | 0.9040 | 0.8077 |
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| 0.1509 | 5.0 | 1975 | 0.3681 | 0.9053 | 0.8088 |
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### Framework versions
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- Transformers 4.18.0
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- Pytorch 1.11.0+cu113
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- Datasets 2.1.0
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- Tokenizers 0.12.1
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