metadata
inference:
parameters:
do_sample: true
max_length: 500
top_p: 0.9
top_k: 20
temperature: 1
num_return_sequences: 10
widget:
- text: >-
abstract: We introduce a new language representation model called BERT,
which stands for Bidirectional Encoder Representations from Transformers.
Unlike recent language representation models, BERT is designed to
pre-train deep bidirectional representations from unlabeled text by
jointly conditioning on both left and right context in all layers. As a
result, the pre-trained BERT model can be fine-tuned with just one
additional output layer to create state-of-the-art models for a wide range
of tasks, such as question answering and language inference, without
substantial task-specific architecture modifications. BERT is conceptually
simple and empirically powerful. It obtains new state-of-the-art results
on eleven natural language processing tasks, including pushing the GLUE
score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to
86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1
to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1
(5.1 point absolute improvement).
example_title: BERT abstract
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("ArvinZhuang/BiTAG-t5-large")
tokenizer = AutoTokenizer.from_pretrained("ArvinZhuang/BiTAG-t5-large")
text = "abstract: [your abstract]" # use 'title:' as the prefix for title_to_abs task.
input_ids = tokenizer.encode(text, return_tensors='pt')
outputs = model.generate(
input_ids,
do_sample=True,
max_length=500,
top_p=0.9,
top_k=20,
temperature=1,
num_return_sequences=10,
)
print("Output:\n" + 100 * '-')
for i, output in enumerate(outputs):
print("{}: {}".format(i+1, tokenizer.decode(output, skip_special_tokens=True)))