metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
base_model: albert-base-v2
model-index:
- name: albert-base-v2-finetuned-rte
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
args: rte
metrics:
- type: accuracy
value: 0.7581227436823105
name: Accuracy
albert-base-v2-finetuned-rte
This model is a fine-tuned version of albert-base-v2 on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 1.2496
- Accuracy: 0.7581
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: 10
- eval_batch_size: 10
- 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 |
|---|---|---|---|---|
| No log | 1.0 | 249 | 0.5914 | 0.6751 |
| No log | 2.0 | 498 | 0.5843 | 0.7184 |
| 0.5873 | 3.0 | 747 | 0.6925 | 0.7220 |
| 0.5873 | 4.0 | 996 | 1.1613 | 0.7545 |
| 0.2149 | 5.0 | 1245 | 1.2496 | 0.7581 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3