tmnam20/VieGLUE
Updated • 30 • 1
How to use tmnam20/bert-base-multilingual-cased-vnrte-100 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="tmnam20/bert-base-multilingual-cased-vnrte-100") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("tmnam20/bert-base-multilingual-cased-vnrte-100")
model = AutoModelForSequenceClassification.from_pretrained("tmnam20/bert-base-multilingual-cased-vnrte-100", device_map="auto")This model is a fine-tuned version of bert-base-multilingual-cased on the tmnam20/VieGLUE/VNRTE 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 | Accuracy |
|---|---|---|---|---|
| 0.0051 | 1.28 | 500 | 0.0040 | 0.9990 |
| 0.0023 | 2.55 | 1000 | 0.0039 | 0.9990 |
Base model
google-bert/bert-base-multilingual-cased