metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: model
results: []
model
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5336
- Accuracy: 0.765
- Precision: {'precision': 0.7894736842105263}
- Recall: {'recall': 0.6593406593406593}
- F1: {'f1': 0.7185628742514969}
- Tp: 60
- Fp: 16
- Tn: 93
- Fn: 31
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: 5e-05
- train_batch_size: 96
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Tp | Fp | Tn | Fn |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6134 | 2.0 | 18 | 0.5336 | 0.765 | {'precision': 0.7894736842105263} | {'recall': 0.6593406593406593} | {'f1': 0.7185628742514969} | 60 | 16 | 93 | 31 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0