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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
base_model: bert-base-uncased
model-index:
- name: sequence_classification
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- type: accuracy
value: 0.8529411764705882
name: Accuracy
- type: f1
value: 0.8943661971830987
name: F1
---
<!-- 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. -->
# sequence_classification
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7738
- Accuracy: 0.8529
- F1: 0.8944
## 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: 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 459 | 0.3519 | 0.8627 | 0.9 |
| 0.4872 | 2.0 | 918 | 0.6387 | 0.8333 | 0.8893 |
| 0.2488 | 3.0 | 1377 | 0.7738 | 0.8529 | 0.8944 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|