README.md

---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-mnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE MNLI
      type: glue
      args: mnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8500813669650122
  - task:
      type: natural-language-inference
      name: Natural Language Inference
    dataset:
      name: glue
      type: glue
      config: mnli_matched
      split: validation
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8467651553744269
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.8460148987014974
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.8467651553744269
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.8475656756385261
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.8463172075485045
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.8467651553744269
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.8467651553744269
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.8459654597797398
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.8467651553744269
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.8469586362613581
      verified: true
    - name: loss
      type: loss
      value: 0.42515239119529724
      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-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: 0.4056
- Accuracy: 0.8501

## 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: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4526        | 1.0   | 12272 | 0.4244          | 0.8388   |
| 0.3344        | 2.0   | 24544 | 0.4252          | 0.8469   |
| 0.2307        | 3.0   | 36816 | 0.4974          | 0.8445   |


### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1