|
--- |
|
license: apache-2.0 |
|
base_model: bert-base-multilingual-cased |
|
tags: |
|
- text-classification |
|
- generated_from_trainer |
|
datasets: |
|
- glue |
|
metrics: |
|
- accuracy |
|
- f1 |
|
model-index: |
|
- name: bert-base-multilingual-cased-mrpc-glue |
|
results: |
|
- task: |
|
name: Text Classification |
|
type: text-classification |
|
dataset: |
|
name: datasetX |
|
type: glue |
|
config: mrpc |
|
split: validation |
|
args: mrpc |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.7426470588235294 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8059149722735676 |
|
--- |
|
|
|
<!-- 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-multilingual-cased-mrpc-glue |
|
|
|
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the datasetX dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5185 |
|
- Accuracy: 0.7426 |
|
- F1: 0.8059 |
|
|
|
## 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 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
|
| 0.604 | 1.09 | 500 | 0.5185 | 0.7426 | 0.8059 | |
|
| 0.4834 | 2.18 | 1000 | 0.5550 | 0.7770 | 0.8544 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.31.0 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.4 |
|
- Tokenizers 0.13.3 |
|
|