|
--- |
|
tags: |
|
- multilingual |
|
datasets: |
|
- xquad |
|
--- |
|
|
|
# bert-base-multilingual-uncased for multilingual QA |
|
|
|
# Overview |
|
**Language Model**: bert-base-multilingual-uncased \ |
|
**Downstream task**: Extractive QA \ |
|
**Training data**: [XQuAD](https://github.com/deepmind/xquad) \ |
|
**Testing Data**: [XQuAD](https://github.com/deepmind/xquad) |
|
|
|
# Hyperparameters |
|
|
|
```python |
|
batch_size = 48 |
|
n_epochs = 6 |
|
max_seq_len = 384 |
|
doc_stride = 128 |
|
learning_rate = 3e-5 |
|
``` |
|
|
|
# Performance |
|
|
|
Evaluated on held-out test set from XQuAD |
|
```python |
|
"exact_match": 64.6067415730337, |
|
"f1": 79.52043478874286, |
|
"test_samples": 2384 |
|
``` |
|
|
|
# Usage |
|
|
|
## In Transformers |
|
```python |
|
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline |
|
|
|
model_name = "alon-albalak/bert-base-multilingual-xquad" |
|
|
|
# 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 |
|
```python |
|
from farm.modeling.adaptive_model import AdaptiveModel |
|
from farm.modeling.tokenization import Tokenizer |
|
from farm.infer import QAInferencer |
|
|
|
model_name = "alon-albalak/bert-base-multilingual-xquad" |
|
|
|
# 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 |
|
|
|
```python |
|
reader = FARMReader(model_name_or_path="alon-albalak/bert-base-multilingual-xquad") |
|
# or |
|
reader = TransformersReader(model="alon-albalak/bert-base-multilingual-xquad",tokenizer="alon-albalak/bert-base-multilingual-xquad") |
|
``` |
|
|
|
Usage instructions for FARM and Haystack were adopted from https://huggingface.co/deepset/xlm-roberta-large-squad2 |
