metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: training
results: []
training
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8713
- Accuracy: 0.5183
- F1: 0.5192
- Precision: 0.5219
- Recall: 0.5183
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: 20
- eval_batch_size: 20
- 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 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 66 | 0.7083 | 0.4970 | 0.4093 | 0.5891 | 0.4970 |
| No log | 2.0 | 132 | 0.7447 | 0.4939 | 0.4486 | 0.5338 | 0.4939 |
| No log | 3.0 | 198 | 0.7978 | 0.5 | 0.4814 | 0.5239 | 0.5 |
| No log | 4.0 | 264 | 0.8450 | 0.5091 | 0.5100 | 0.5136 | 0.5091 |
| No log | 5.0 | 330 | 0.8713 | 0.5183 | 0.5192 | 0.5219 | 0.5183 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0