metadata
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE RTE
type: glue
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.6895306859205776
- task:
type: natural-language-inference
name: Natural Language Inference
dataset:
name: glue
type: glue
config: rte
split: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.6823104693140795
verified: true
- name: Precision
type: precision
value: 0.7047619047619048
verified: true
- name: Recall
type: recall
value: 0.5648854961832062
verified: true
- name: AUC
type: auc
value: 0.7394646031580048
verified: true
- name: F1
type: f1
value: 0.6271186440677967
verified: true
- name: loss
type: loss
value: 0.7001310586929321
verified: true
bert-base-uncased-rte
This model is a fine-tuned version of bert-base-uncased on the GLUE RTE dataset. It achieves the following results on the evaluation set:
- Loss: 0.6972
- Accuracy: 0.6895
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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 156 | 0.6537 | 0.6318 |
| No log | 2.0 | 312 | 0.6383 | 0.6534 |
| No log | 3.0 | 468 | 0.6972 | 0.6895 |
Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1