Edit model card

xlm-roberta-large for multilingual QA

Overview

Language Model: xlm-roberta-large
Downstream task: Extractive QA
Training data: XQuAD
Testing Data: XQuAD

Hyperparameters

batch_size = 48
n_epochs = 13
max_seq_len = 384
doc_stride = 128
learning_rate = 3e-5

Performance

Evaluated on held-out test set from XQuAD

"exact_match": 87.12546816479401,
"f1": 94.77703248802527,
"test_samples": 2307

Usage

In Transformers

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "alon-albalak/xlm-roberta-large-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

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

model_name = "alon-albalak/xlm-roberta-large-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

reader = FARMReader(model_name_or_path="alon-albalak/xlm-roberta-large-xquad")
# or 
reader = TransformersReader(model="alon-albalak/xlm-roberta-large-xquad",tokenizer="alon-albalak/xlm-roberta-large-xquad")

Usage instructions for FARM and Haystack were adopted from https://huggingface.co/deepset/xlm-roberta-large-squad2

Downloads last month
108
Safetensors
Model size
559M params
Tensor type
I64
·
F32
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train alon-albalak/xlm-roberta-large-xquad

Space using alon-albalak/xlm-roberta-large-xquad 1