deepset_-_roberta-large-squad2-4bits

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roberta-large-squad2 - bnb 4bits

• Model creator: https://huggingface.co/deepset/
• Original model: https://huggingface.co/deepset/roberta-large-squad2/

Original model description:

language: en license: cc-by-4.0 datasets:

• squad_v2 base_model: roberta-large model-index:
• name: deepset/roberta-large-squad2 results:
  • task: type: question-answering name: Question Answering dataset: name: squad_v2 type: squad_v2 config: squad_v2 split: validation metrics:
    • type: exact_match value: 85.168 name: Exact Match
    • type: f1 value: 88.349 name: F1
  • task: type: question-answering name: Question Answering dataset: name: squad type: squad config: plain_text split: validation metrics:
    • type: exact_match value: 87.162 name: Exact Match
    • type: f1 value: 93.603 name: F1
  • task: type: question-answering name: Question Answering dataset: name: adversarial_qa type: adversarial_qa config: adversarialQA split: validation metrics:
    • type: exact_match value: 35.900 name: Exact Match
    • type: f1 value: 48.923 name: F1
  • task: type: question-answering name: Question Answering dataset: name: squad_adversarial type: squad_adversarial config: AddOneSent split: validation metrics:
    • type: exact_match value: 81.142 name: Exact Match
    • type: f1 value: 87.099 name: F1
  • task: type: question-answering name: Question Answering dataset: name: squadshifts amazon type: squadshifts config: amazon split: test metrics:
    • type: exact_match value: 72.453 name: Exact Match
    • type: f1 value: 86.325 name: F1
  • task: type: question-answering name: Question Answering dataset: name: squadshifts new_wiki type: squadshifts config: new_wiki split: test metrics:
    • type: exact_match value: 82.338 name: Exact Match
    • type: f1 value: 91.974 name: F1
  • task: type: question-answering name: Question Answering dataset: name: squadshifts nyt type: squadshifts config: nyt split: test metrics:
    • type: exact_match value: 84.352 name: Exact Match
    • type: f1 value: 92.645 name: F1
  • task: type: question-answering name: Question Answering dataset: name: squadshifts reddit type: squadshifts config: reddit split: test metrics:
    • type: exact_match value: 74.722 name: Exact Match
    • type: f1 value: 86.860 name: F1

---

roberta-large for QA

This is the roberta-large 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-large
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

base_LM_model = "roberta-large"

Using a distilled model instead

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

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

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

In Transformers

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "deepset/roberta-large-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)

Authors

Branden Chan: [email protected]
Timo Möller: [email protected]
Malte Pietsch: [email protected]
Tanay Soni: [email protected]

About us

<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> </div> </div>

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:

• Distilled roberta-base-squad2 (aka "tinyroberta-squad2")
• German BERT (aka "bert-base-german-cased")
• GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")

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!