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