transformers pytorch tensorboard bert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
language:
- en
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-mnli
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE MNLI
      type: glue
      config: mnli
      split: validation_matched
      args: mnli
    metrics:
    - type: accuracy
      value: 0.8487591537835639
      name: Accuracy
---

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# bert-base-uncased-mnli

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4218
- Accuracy: 0.8488

## 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: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5194        | 1.0   | 3068  | 0.4468          | 0.8307   |
| 0.3445        | 2.0   | 6136  | 0.4384          | 0.8428   |
| 0.2341        | 3.0   | 9204  | 0.4946          | 0.8415   |
| 0.1625        | 4.0   | 12272 | 0.5479          | 0.8388   |
| 0.1218        | 5.0   | 15340 | 0.6348          | 0.8358   |
| 0.0968        | 6.0   | 18408 | 0.6620          | 0.8315   |
| 0.0799        | 7.0   | 21476 | 0.7072          | 0.8287   |
| 0.0675        | 8.0   | 24544 | 0.7659          | 0.8307   |
| 0.0592        | 9.0   | 27612 | 0.7978          | 0.8305   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
