klue/klue
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How to use ys7yoo/sts_klue_roberta_large_ep5_ckpt with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="ys7yoo/sts_klue_roberta_large_ep5_ckpt") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ys7yoo/sts_klue_roberta_large_ep5_ckpt")
model = AutoModelForSequenceClassification.from_pretrained("ys7yoo/sts_klue_roberta_large_ep5_ckpt", device_map="auto")This model is a fine-tuned version of klue/roberta-large on the klue 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 | Mse | Mae | R2 |
|---|---|---|---|---|---|---|
| 1.1257 | 1.0 | 183 | 0.9606 | 0.9606 | 0.7609 | 0.5601 |
| 0.1867 | 2.0 | 366 | 0.4631 | 0.4631 | 0.5015 | 0.7879 |
| 0.1135 | 3.0 | 549 | 0.3242 | 0.3242 | 0.4207 | 0.8515 |
| 0.0742 | 4.0 | 732 | 0.3815 | 0.3815 | 0.4613 | 0.8253 |
| 0.0471 | 5.0 | 915 | 0.3633 | 0.3633 | 0.4492 | 0.8336 |
Base model
klue/roberta-large