tmnam20/VieGLUE
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How to use tmnam20/bert-base-multilingual-cased-cola-1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="tmnam20/bert-base-multilingual-cased-cola-1") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("tmnam20/bert-base-multilingual-cased-cola-1")
model = AutoModelForSequenceClassification.from_pretrained("tmnam20/bert-base-multilingual-cased-cola-1")This model is a fine-tuned version of bert-base-multilingual-cased on the tmnam20/VieGLUE/COLA dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.6099 | 1.87 | 500 | 0.6055 | 0.0 |
Base model
google-bert/bert-base-multilingual-cased