XLNet: Generalized Autoregressive Pretraining for Language Understanding
Paper • 1906.08237 • Published • 1
How to use ggoggam/xlnet-base-cased-squad-quoref with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("question-answering", model="ggoggam/xlnet-base-cased-squad-quoref") # Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("ggoggam/xlnet-base-cased-squad-quoref")
model = AutoModelForQuestionAnswering.from_pretrained("ggoggam/xlnet-base-cased-squad-quoref", device_map="auto")YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
XLNet jointly developed by Google and CMU and fine-tuned on SQuAD / SQuAD 2.0 and Quoref for question answering down-stream task.
{
"exact_match": 73.65591397848462,
"f1": 77.9981532789881
}
| Metric | XLNet Base Line | Model FT on SQuAD |
|---|---|---|
| EM | 61.88 | 73.66 (+11.78) |
| F1 | 70.51 | 78.00 (+7.49) |
from transformers import XLNetForQuestionAnswering, XLNetTokenizerFast
model = XLNetForQuestionAnswering.from_pretrained('jkgrad/xlnet-base-cased-squad-quoref)
tokenizer = XLNetTokenizerFast.from_pretrained('jkgrad/xlnet-base-cased-squad-quoref')