language: | |
- en | |
license: apache-2.0 | |
tags: | |
- generated_from_trainer | |
datasets: | |
- glue | |
metrics: | |
- accuracy | |
- f1 | |
model-index: | |
- name: bert-base-uncased-mrpc | |
results: | |
- task: | |
name: Text Classification | |
type: text-classification | |
dataset: | |
name: GLUE MRPC | |
type: glue | |
args: mrpc | |
metrics: | |
- name: Accuracy | |
type: accuracy | |
value: 0.8602941176470589 | |
- name: F1 | |
type: f1 | |
value: 0.9042016806722689 | |
- task: | |
type: natural-language-inference | |
name: Natural Language Inference | |
dataset: | |
name: glue | |
type: glue | |
config: mrpc | |
split: validation | |
metrics: | |
- name: Accuracy | |
type: accuracy | |
value: 0.8602941176470589 | |
verified: true | |
- name: Precision | |
type: precision | |
value: 0.8512658227848101 | |
verified: true | |
- name: Recall | |
type: recall | |
value: 0.96415770609319 | |
verified: true | |
- name: AUC | |
type: auc | |
value: 0.8985718651885194 | |
verified: true | |
- name: F1 | |
type: f1 | |
value: 0.9042016806722689 | |
verified: true | |
- name: loss | |
type: loss | |
value: 0.6978028416633606 | |
verified: true | |
<!-- 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-mrpc | |
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset. | |
It achieves the following results on the evaluation set: | |
- Loss: 0.6978 | |
- Accuracy: 0.8603 | |
- F1: 0.9042 | |
- Combined Score: 0.8822 | |
### Training hyperparameters | |
The following hyperparameters were used during training: | |
- learning_rate: 2e-05 | |
- train_batch_size: 16 | |
- eval_batch_size: 8 | |
- seed: 42 | |
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
- lr_scheduler_type: linear | |
- num_epochs: 5.0 | |
### Framework versions | |
- Transformers 4.17.0 | |
- Pytorch 1.10.0+cu102 | |
- Datasets 1.14.0 | |
- Tokenizers 0.11.6 | |