bert_multilingual / README.md
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---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- nsmc
metrics:
- accuracy
model-index:
- name: roberta
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: nsmc
type: nsmc
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.86608
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# roberta
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the nsmc dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3346
- Accuracy: 0.8661
## 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-06
- 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: cosine
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3619 | 1.0 | 9375 | 0.3406 | 0.8516 |
| 0.2989 | 2.0 | 18750 | 0.3243 | 0.8644 |
| 0.2655 | 3.0 | 28125 | 0.3346 | 0.8661 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3