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jiiyy
/
bert_multilingual

Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
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1
How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="jiiyy/bert_multilingual")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("jiiyy/bert_multilingual")
model = AutoModelForSequenceClassification.from_pretrained("jiiyy/bert_multilingual")
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roberta

This model is a fine-tuned version of 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
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Base model

google-bert/bert-base-multilingual-cased
Finetuned
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Dataset used to train jiiyy/bert_multilingual

Evaluation results