metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- liar
metrics:
- accuracy
model-index:
- name: liar_binaryclassifier_bert_cased
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: liar
type: liar
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.648590021691974
liar_binaryclassifier_bert_cased
This model is a fine-tuned version of bert-base-cased on the liar dataset. It achieves the following results on the evaluation set:
- Loss: 0.6331
- Model Preparation Time: 0.0032
- Accuracy: 0.6486
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: 3e-06
- train_batch_size: 8
- eval_batch_size: 8
- 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 | Model Preparation Time | Accuracy |
|---|---|---|---|---|---|
| 0.6826 | 1.0 | 461 | 0.6477 | 0.0032 | 0.6117 |
| 0.6435 | 2.0 | 922 | 0.6267 | 0.0032 | 0.6356 |
| 0.6131 | 3.0 | 1383 | 0.6302 | 0.0032 | 0.6529 |
| 0.5809 | 4.0 | 1844 | 0.6233 | 0.0032 | 0.6508 |
| 0.5658 | 5.0 | 2305 | 0.6331 | 0.0032 | 0.6486 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1