question-answering datasets:
squad_v2 model-index:
name: deepset/xlm-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: 81.8281 name: Exact Match verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzVhZDE2NTg5NmUwOWRkMmI2MGUxYjFlZjIzNmMyNDQ2MDY2MDNhYzE0ZjY5YTkyY2U4ODc3ODFiZjQxZWQ2YSIsInZlcnNpb24iOjF9.f_rN3WPMAdv-OBPz0T7N7lOxYz9f1nEr_P-vwKhi3jNdRKp_JTy18MYR9eyJM2riKHC6_ge-8XwfyrUf51DSDA
  - type: f1 value: 84.8886 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGE5MWJmZGUxMGMwNWFhYzVhZjQwZGEwOWQ4N2Q2Yjg5NzdjNDFiNDhiYTQ1Y2E5ZWJkOTFhYmI1Y2Q2ZGYwOCIsInZlcnNpb24iOjF9.TIdH-tOx3kEMDs5wK1r6iwZqqSjNGlBrpawrsE917j1F3UFJVnQ7wJwaj0OIgmC4iw8OQeLZL56ucBcLApa-AQ

Multilingual XLM-RoBERTa large for QA on various languages

Overview

Language model: xlm-roberta-large
Language: Multilingual
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD dev set - German MLQA - German XQuAD
Training run: MLFlow link
Infrastructure: 4x Tesla v100

Hyperparameters

batch_size = 32
n_epochs = 3
base_LM_model = "xlm-roberta-large"
max_seq_len = 256
learning_rate = 1e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride=128
max_query_length=64

Performance

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

  "exact": 79.45759285774446,
  "f1": 83.79259828925511,
  "total": 11873,
  "HasAns_exact": 71.96356275303644,
  "HasAns_f1": 80.6460053117963,
  "HasAns_total": 5928,
  "NoAns_exact": 86.93019343986543,
  "NoAns_f1": 86.93019343986543,
  "NoAns_total": 5945

Evaluated on German MLQA: test-context-de-question-de.json

"exact": 49.34691166703564,
"f1": 66.15582561674236,
"total": 4517,

Evaluated on German XQuAD: xquad.de.json

"exact": 61.51260504201681,
"f1": 78.80206098332569,
"total": 1190,

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

In Transformers

python

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "deepset/xlm-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 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

For more info on Haystack, visit our GitHub repo and Documentation.

We also have a Discord community open to everyone!

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By the way: we're hiring!

RichardErkhov/deepset_-_xlm-roberta-large-squad2-8bits

Original model description: