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RichardErkhov/deepset_-_roberta-base-squad2-covid-4bits

sourceHugging Faceupdated 2y agoView on Hugging Face
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roberta-base-squad2-covid - bnb 4bits

  • —Model creator: https://huggingface.co/deepset/
  • —Original model: https://huggingface.co/deepset/roberta-base-squad2-covid/

Original model description: --- language: en datasets:

  • —squad_v2 license: cc-by-4.0 ---

roberta-base-squad2 for QA on COVID-19

Overview

Language model: deepset/roberta-base-squad2 Language: English Downstream-task: Extractive QA Training data: SQuAD-style CORD-19 annotations from 23rd April Code: See an example QA pipeline on Haystack Infrastructure: Tesla v100

Hyperparameters

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

license: cc-by-4.0 ---

Performance

5-fold cross-validation on the data set led to the following results:

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

This model is the model obtained from the third fold of the cross-validation.

Usage

In Haystack

For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack:

python
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")

In Transformers

python
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)

Authors

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

About us

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deepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.

Some of our other work:

Get in touch and join the Haystack community

<p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>.

We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join">Discord community open to everyone!</a></strong></p>

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