transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert-large-uncased-finetuned-rte
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: rte
      split: validation
      args: rte
    metrics:
    - type: accuracy
      value: 0.7545126353790613
      name: Accuracy
---

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# bert-large-uncased-finetuned-rte

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7653
- Accuracy: 0.7545

## 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: 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 312  | 0.6211          | 0.6643   |
| 0.6082        | 2.0   | 624  | 0.6857          | 0.6643   |
| 0.6082        | 3.0   | 936  | 0.7653          | 0.7545   |
| 0.3509        | 4.0   | 1248 | 1.2287          | 0.7401   |
| 0.153         | 5.0   | 1560 | 1.5192          | 0.7437   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
