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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8480392156862745
- name: F1
type: f1
value: 0.8934707903780068
---
<!-- 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. -->
# distilbert-mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6783
- Accuracy: 0.8480
- F1: 0.8935
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5916 | 0.22 | 100 | 0.5676 | 0.7157 | 0.8034 |
| 0.5229 | 0.44 | 200 | 0.4534 | 0.7770 | 0.8212 |
| 0.5055 | 0.65 | 300 | 0.4037 | 0.8137 | 0.8762 |
| 0.4597 | 0.87 | 400 | 0.3706 | 0.8407 | 0.8893 |
| 0.4 | 1.09 | 500 | 0.4590 | 0.8113 | 0.8566 |
| 0.3498 | 1.31 | 600 | 0.4196 | 0.8554 | 0.8974 |
| 0.2916 | 1.53 | 700 | 0.4606 | 0.8554 | 0.8933 |
| 0.3309 | 1.74 | 800 | 0.5162 | 0.8578 | 0.9027 |
| 0.3788 | 1.96 | 900 | 0.3911 | 0.8529 | 0.8980 |
| 0.2059 | 2.18 | 1000 | 0.5842 | 0.8554 | 0.8995 |
| 0.1595 | 2.4 | 1100 | 0.5701 | 0.8578 | 0.8975 |
| 0.1205 | 2.61 | 1200 | 0.6905 | 0.8407 | 0.8889 |
| 0.174 | 2.83 | 1300 | 0.6783 | 0.8480 | 0.8935 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
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