Quantization made by Richard Erkhov.
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: branden.chan@deepset.ai
Timo M枚ller: 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:
- 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
For more info on Haystack, visit our GitHub repo and Documentation.
We also have a Discord community open to everyone!
Twitter | LinkedIn | Discord | GitHub Discussions | Website
By the way: we're hiring!
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