Dotted-WSD
Collection
Models that disambiguate word sense and regular polysemy. • 13 items • Updated
How to use lopentu/IDEA-CCNL-Erlangshen-DeBERTa-v2-97M-Chinese-DottedWSD with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="lopentu/IDEA-CCNL-Erlangshen-DeBERTa-v2-97M-Chinese-DottedWSD") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("lopentu/IDEA-CCNL-Erlangshen-DeBERTa-v2-97M-Chinese-DottedWSD")
model = AutoModelForSequenceClassification.from_pretrained("lopentu/IDEA-CCNL-Erlangshen-DeBERTa-v2-97M-Chinese-DottedWSD")This model is a fine-tuned version of IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese on the None 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 |
|---|---|---|---|---|
| 0.1805 | 0.9997 | 770 | 0.1712 | 0.9310 |
| 0.1368 | 1.9994 | 1540 | 0.1511 | 0.9391 |
| 0.1049 | 2.9990 | 2310 | 0.1629 | 0.9399 |
Base model
IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese