metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eoir_privacy
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-eoir_privacy
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: eoir_privacy
type: eoir_privacy
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.9052835051546392
- name: F1
type: f1
value: 0.8088426527958388
distilbert-base-uncased-finetuned-eoir_privacy
This model is a fine-tuned version of distilbert-base-uncased on the eoir_privacy dataset. It achieves the following results on the evaluation set:
- Loss: 0.3681
- Accuracy: 0.9053
- F1: 0.8088
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 395 | 0.3053 | 0.8789 | 0.7432 |
| 0.3562 | 2.0 | 790 | 0.2857 | 0.8976 | 0.7883 |
| 0.2217 | 3.0 | 1185 | 0.3358 | 0.8905 | 0.7550 |
| 0.1509 | 4.0 | 1580 | 0.3505 | 0.9040 | 0.8077 |
| 0.1509 | 5.0 | 1975 | 0.3681 | 0.9053 | 0.8088 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1