roberta-base-squad2 用于COVID-19的问答（QA）

概览

语言模型: deepset/roberta-base-squad2 语言: 英语下游任务: 抽取式问答训练数据: SQuAD-style CORD-19 annotations from 23rd April 代码: 参见 an example QA pipeline on Haystack 基础架构: Tesla v100

超参数

batch_size = 24
n_epochs = 3
base_LM_model = "deepset/roberta-base-squad2"
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.1
doc_stride = 128
xval_folds = 5
dev_split = 0
no_ans_boost = -100

许可证: cc-by-4.0

性能

在数据集上进行的5折交叉验证导致以下结果:

单个EM分数: [0.222, 0.123, 0.234, 0.159, 0.158] 单个F1分数: [0.476, 0.493, 0.599, 0.461, 0.465] 单个top\_3\_recall分数: [0.827, 0.776, 0.860, 0.771, 0.777] XVAL EM: 0.17890995260663506 XVAL f1: 0.49925444207319924 XVAL top\_3\_recall: 0.8021327014218009

此模型是交叉验证的第三折的模型。

用法

在Haystack中

用于大规模QA（即多个文档而不是单个段落）时，您还可以在 haystack 中加载模型：

reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2-covid")
# or 
reader = TransformersReader(model="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2-covid")

在Transformers中

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline


model_name = "deepset/roberta-base-squad2-covid"

# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
    'question': 'Why is model conversion important?',
    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
}
res = nlp(QA_input)

# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

作者

Branden Chan: branden.chan@deepset.ai Timo Möller: timo.moeller@deepset.ai Malte Pietsch: malte.pietsch@deepset.ai Tanay Soni: tanay.soni@deepset.ai Bogdan Kostić: bogdan.kostic@deepset.ai