metadata

license: mit
tags:
  - Keyphrase Generation

Usage

!pip install KeyBartAdapter

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from models import KeyBartAdapter

model = KeyBartAdapter.from_pretrained('Adapting/KeyBartAdapter', revision = '3aee5ecf1703b9955ab0cd1b23208cc54eb17fce',adapter_hid_dim =32)
tokenizer = AutoTokenizer.from_pretrained("bloomberg/KeyBART")

adapter layer hd 512 init model: e38c77df86e0e289e5846455e226f4e9af09ef8e
adapter layer hd 256 init model: c6f3b357d953dcb5943b6333a0f9f941b832477
adapter layer hd 128 init model: f88116fa1c995f07ccd5ad88862e0aa4f162b1ea
adapter layer hd 64 init model: f7e8c6323b8d5822667ddc066ffe19ac7b810f4a
adapter layer hd 32 init model: 24ec15daef1670fb9849a56517a6886b69b652f6

1. inference

from transformers import Text2TextGenerationPipeline
pipe = Text2TextGenerationPipeline(model=model,tokenizer=tokenizer)

abstract = '''Non-referential face image quality assessment methods have gained popularity as a pre-filtering step on face recognition systems. In most of them, the quality score is usually designed with face matching in mind. However, a small amount of work has been done on measuring their impact and usefulness on Presentation Attack Detection (PAD). In this paper, we study the effect of quality assessment methods on filtering bona fide and attack samples, their impact on PAD systems, and how the performance of such systems is improved when training on a filtered (by quality) dataset. On a Vision Transformer PAD algorithm, a reduction of 20% of the training dataset by removing lower quality samples allowed us to improve the BPCER by 3% in a cross-dataset test.'''

pipe(abstract)