YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/model-cards#model-card-metadata)
XLNet Fine-tuned on SQuAD 2.0 Dataset
XLNet jointly developed by Google and CMU and fine-tuned on SQuAD 2.0 for question answering down-stream task.
Training Results (Metrics)
{
"HasAns_exact": 74.7132253711201
"HasAns_f1": 82.11971607032643
"HasAns_total": 5928
"NoAns_exact": 73.38940285954584
"NoAns_f1": 73.38940285954584
"NoAns_total": 5945
"best_exact": 75.67590331003116
"best_exact_thresh": -19.554906845092773
"best_f1": 79.16215426779269
"best_f1_thresh": -19.554906845092773
"epoch": 4.0
"exact": 74.05036637749515
"f1": 77.74830934598614
"total": 11873
}
Results Comparison
Metric | Paper | Model |
---|---|---|
EM | 78.46 | 75.68 (-2.78) |
F1 | 81.33 | 79.16 (-2.17) |
Better fine-tuned models coming soon.
How to Use
from transformers import XLNetForQuestionAnswering, XLNetTokenizerFast
model = XLNetForQuestionAnswering.from_pretrained('jkgrad/xlnet-base-squadv2)
tokenizer = XLNetTokenizerFast.from_pretrained('jkgrad/xlnet-base-squadv2')
- Downloads last month
- 39
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.