metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.7328431372549019
- name: F1
type: f1
value: 0.8310077519379845
distilbert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.5579
- Accuracy: 0.7328
- F1: 0.8310
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 23 | 0.5797 | 0.7010 | 0.8195 |
| No log | 2.0 | 46 | 0.5647 | 0.7083 | 0.8242 |
| No log | 3.0 | 69 | 0.5677 | 0.7181 | 0.8276 |
| No log | 4.0 | 92 | 0.5495 | 0.7328 | 0.8300 |
| No log | 5.0 | 115 | 0.5579 | 0.7328 | 0.8310 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3