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xlm-roberta-base-squad2-distilled

Question Answering
Transformers
PyTorch
Safetensors
multilingual
xlm-roberta
exbert
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xlm-roberta-base-squad2-distilled / README.md
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Add evaluation results on the squad_v2 config of squad_v2
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metadata
language: multilingual
datasets:
  - squad_v2
license: mit
thumbnail: >-
  https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
tags:
  - exbert
model-index:
  - name: deepset/xlm-roberta-base-squad2-distilled
    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: 75.2633
            verified: true
          - name: F1
            type: f1
            value: 78.3188
            verified: true

deepset/xlm-roberta-base-squad2-distilled

  • haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model.

Overview

Language model: deepset/xlm-roberta-base-squad2-distilled
Language: Multilingual
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Code: See an example QA pipeline on Haystack
Infrastructure: 1x Tesla v100

Hyperparameters 

batch_size = 56
n_epochs = 4
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
embeds_dropout_prob = 0.1
temperature = 3
distillation_loss_weight = 0.75

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

For a complete example of deepset/xlm-roberta-base-squad2-distilled being used for [question answering], check out the Tutorials in Haystack Documentation

In Transformers 

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "deepset/xlm-roberta-base-squad2-distilled"

# 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

"exact": 74.06721131980123%
"f1": 76.39919553344667%

Authors

Timo Möller: timo.moeller@deepset.ai
Julian Risch: julian.risch@deepset.ai
Malte Pietsch: malte.pietsch@deepset.ai
Michel Bartels: michel.bartels@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!