Dotted-WSD
Collection
Models that disambiguate word sense and regular polysemy. • 13 items • Updated
How to use lopentu/microsoft-mdeberta-v3-base-DottedWSD with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="lopentu/microsoft-mdeberta-v3-base-DottedWSD") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("lopentu/microsoft-mdeberta-v3-base-DottedWSD")
model = AutoModelForSequenceClassification.from_pretrained("lopentu/microsoft-mdeberta-v3-base-DottedWSD")This model is a fine-tuned version of microsoft/mdeberta-v3-base 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 |
|---|---|---|---|---|
| 0.2075 | 0.9997 | 770 | 0.1969 | 0.9197 |
| 0.1838 | 1.9994 | 1540 | 0.1815 | 0.9279 |
| 0.1888 | 2.9990 | 2310 | 0.1818 | 0.9279 |
Base model
microsoft/mdeberta-v3-base