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---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: jrtec-distilroberta-base-mrpc-glue-omar-espejel
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: datasetX
type: glue
config: mrpc
split: train
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8161764705882353
- name: F1
type: f1
value: 0.8747913188647747
---
<!-- 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. -->
# jrtec-distilroberta-base-mrpc-glue-omar-espejel
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4901
- Accuracy: 0.8162
- F1: 0.8748
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4845 | 1.09 | 500 | 0.4901 | 0.8162 | 0.8748 |
| 0.3706 | 2.18 | 1000 | 0.6421 | 0.8162 | 0.8691 |
| 0.2003 | 3.27 | 1500 | 0.9711 | 0.8162 | 0.8760 |
| 0.1281 | 4.36 | 2000 | 0.8224 | 0.8480 | 0.8893 |
| 0.0717 | 5.45 | 2500 | 1.1803 | 0.8113 | 0.8511 |
| 0.0344 | 6.54 | 3000 | 1.1759 | 0.8480 | 0.8935 |
| 0.0277 | 7.63 | 3500 | 1.2140 | 0.8456 | 0.8927 |
| 0.0212 | 8.71 | 4000 | 1.0895 | 0.8554 | 0.8974 |
| 0.0071 | 9.8 | 4500 | 1.1849 | 0.8554 | 0.8991 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|