metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
- precision
- recall
base_model: bert-large-uncased
model-index:
- name: trainer
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- type: accuracy
value: 0.8465963566634708
name: Accuracy
- type: f1
value: 0.8064540073113251
name: F1
- type: precision
value: 0.840606542828289
name: Precision
- type: recall
value: 0.7876439727431708
name: Recall
trainer
This model is a fine-tuned version of bert-large-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.4490
- Accuracy: 0.8466
- F1: 0.8065
- Precision: 0.8406
- Recall: 0.7876
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: 8
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 267 | 0.3860 | 0.8370 | 0.7999 | 0.8184 | 0.7876 |
| 0.3455 | 2.0 | 534 | 0.4490 | 0.8466 | 0.8065 | 0.8406 | 0.7876 |
Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1