---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-base-uncased-rte

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/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