Model: ielabgroup/BiTAG-t5-large
Author: ielabgroup
Library: transformers
Tags: transformers, pytorch, t5, text2text-generation, text-generation-inference, endpoints_compatible, region:us
Card metadata: inference: parameters: do_sample: yes
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
Likes: 1 | All-time downloads: 1355
Created: 2022-03-02 23:29:04