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

sourceHugging Faceupdated 2y agoView on Hugging Face

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roberta-base-squad2 - bnb 4bits

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

Original model description: --- language: en license: cc-by-4.0 datasets:

—squad_v2 model-index:
—name: deepset/roberta-base-squad2 results:
—task: type: question-answering name: Question Answering dataset: name: squadv2 type: squadv2 config: squad_v2 split: validation metrics:
—type: exactmatch value: 79.9309 name: Exact Match verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDhhNjg5YzNiZGQ1YTIyYTAwZGUwOWEzZTRiYzdjM2QzYjA3ZTUxNDM1NjE1MTUyMjE1MGY1YzEzMjRjYzVjYiIsInZlcnNpb24iOjF9.EH5JJo8EEFwU7osPz3s7qanwtigeCFhCXjSfyN0Y1nWVnSfulSxIk_DbAEI5iE80V4EKLyp5-mYFodWvL2KDA
—type: f1 value: 82.9501 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjk5ZDYwOGQyNjNkMWI0OTE4YzRmOTlkY2JjNjQ0YTZkNTMzMzNkYTA0MDFmNmI3NjA3NjNlMjhiMDQ2ZjJjNSIsInZlcnNpb24iOjF9.DDm0LNTkdLbGsue58bg1aHs67KfbcmkvL-6ZiI2s8IoxhHJMSf29HuV2YLyevwx900t-MwTVOW3qfFnMMEAQ
—type: total value: 11869 name: total verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGFkMmI2ODM0NmY5NGNkNmUxYWViOWYxZDNkY2EzYWFmOWI4N2VhYzY5MGEzMTVhOTU4Zjc4YWViOGNjOWJjMCIsInZlcnNpb24iOjF9.fexrU1icJK5MiifBtZWkeUvpmFISqBLDXSQJ8E6UnrRof-7cU0s4tXdIsauHWtUpIHMPZCf5dlMWQKXZuAAA
—task: type: question-answering name: Question Answering dataset: name: squad type: squad config: plain_text split: validation metrics:
—type: exact_match value: 85.289 name: Exact Match
—type: f1 value: 91.841 name: F1
—task: type: question-answering name: Question Answering dataset: name: adversarialqa type: adversarialqa config: adversarialQA split: validation metrics:
—type: exact_match value: 29.500 name: Exact Match
—type: f1 value: 40.367 name: F1
—task: type: question-answering name: Question Answering dataset: name: squadadversarial type: squadadversarial config: AddOneSent split: validation metrics:
—type: exact_match value: 78.567 name: Exact Match
—type: f1 value: 84.469 name: F1
—task: type: question-answering name: Question Answering dataset: name: squadshifts amazon type: squadshifts config: amazon split: test metrics:
—type: exact_match value: 69.924 name: Exact Match
—type: f1 value: 83.284 name: F1
—task: type: question-answering name: Question Answering dataset: name: squadshifts newwiki type: squadshifts config: newwiki split: test metrics:
—type: exact_match value: 81.204 name: Exact Match
—type: f1 value: 90.595 name: F1
—task: type: question-answering name: Question Answering dataset: name: squadshifts nyt type: squadshifts config: nyt split: test metrics:
—type: exact_match value: 82.931 name: Exact Match
—type: f1 value: 90.756 name: F1
—task: type: question-answering name: Question Answering dataset: name: squadshifts reddit type: squadshifts config: reddit split: test metrics:
—type: exact_match value: 71.550 name: Exact Match
—type: f1 value: 82.939 name: F1 ---

roberta-base for QA

This is the roberta-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.

Overview

Language model: roberta-base Language: English Downstream-task: Extractive QA Training data: SQuAD 2.0 Eval data: SQuAD 2.0 Code: See an example QA pipeline on Haystack Infrastructure: 4x Tesla v100

Hyperparameters

batch_size = 96
n_epochs = 2
base_LM_model = "roberta-base"
max_seq_len = 386
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride=128
max_query_length=64

Using a distilled model instead

Please note that we have also released a distilled version of this model called deepset/tinyroberta-squad2. The distilled model has a comparable prediction quality and runs at twice the speed of the base model.

Usage

In Haystack

Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:

python

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

For a complete example of `roberta-base-squad2` being used for Question Answering, check out the Tutorials in Haystack Documentation

In Transformers

python

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "deepset/roberta-base-squad2"

# 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)

Performance

Evaluated on the SQuAD 2.0 dev set with the official eval script.

"exact": 79.87029394424324,
"f1": 82.91251169582613,

"total": 11873,
"HasAns_exact": 77.93522267206478,
"HasAns_f1": 84.02838248389763,
"HasAns_total": 5928,
"NoAns_exact": 81.79983179142137,
"NoAns_f1": 81.79983179142137,
"NoAns_total": 5945

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

About us

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">Discord community open to everyone!</a></strong></p>

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By the way: we're hiring!