tmnam20/VieGLUE
Updated • 32 • 1
How to use tmnam20/mdeberta-v3-base-vsfc-100 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="tmnam20/mdeberta-v3-base-vsfc-100") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("tmnam20/mdeberta-v3-base-vsfc-100")
model = AutoModelForSequenceClassification.from_pretrained("tmnam20/mdeberta-v3-base-vsfc-100", device_map="auto")This model is a fine-tuned version of microsoft/mdeberta-v3-base on the tmnam20/VieGLUE/VSFC 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 |
|---|---|---|---|---|
| 0.1763 | 1.4 | 500 | 0.2099 | 0.9431 |
| 0.1363 | 2.79 | 1000 | 0.2278 | 0.9463 |
Base model
microsoft/mdeberta-v3-base