metadata

language: multilingual
tags:
  - question-answering
datasets:
  - squad_v2
license: cc-by-4.0
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:
          - name: Exact Match
            type: exact_match
            value: 81.8281
            verified: true
          - name: F1
            type: f1
            value: 84.8886
            verified: true
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: adversarial_qa
          type: adversarial_qa
          config: adversarialQA
          split: validation
        metrics:
          - name: Exact Match
            type: exact_match
            value: 30.2333
            verified: true
          - name: F1
            type: f1
            value: 43.3606
            verified: true

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 Transformers

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)

In FARM

from farm.modeling.adaptive_model import AdaptiveModel
from farm.modeling.tokenization import Tokenizer
from farm.infer import QAInferencer

model_name = "deepset/xlm-roberta-large-squad2"

# a) Get predictions
nlp = QAInferencer.load(model_name)
QA_input = [{"questions": ["Why is model conversion important?"],
             "text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}]
res = nlp.inference_from_dicts(dicts=QA_input, rest_api_schema=True)

# b) Load model & tokenizer
model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering")
tokenizer = Tokenizer.load(model_name)

In haystack

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

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")

Authors

Branden Chan: branden.chan [at] deepset.ai
Timo Möller: timo.moeller [at] deepset.ai
Malte Pietsch: malte.pietsch [at] deepset.ai
Tanay Soni: tanay.soni [at] deepset.ai

About us

We bring NLP to the industry via open source!
Our focus: Industry specific language models & large scale QA systems.

Some of our work:

Get in touch: Twitter | LinkedIn | Slack | GitHub Discussions | Website

By the way: we're hiring!