xlm roberta base squad2

提供商deepset
分类question-answering
许可证cc-by-4.0
下载量1.1K
星标0

简介

xlm-roberta-base-squad2 是一款基于多语言 RoBERTa 预训练模型、并在 SQuAD 2.0 数据集上微调的阅读理解模型。它最核心的竞争力在于强大的跨语言能力，能够处理包括中文在内的多种语言问答任务。与基础模型不同，它经过专门训练，能够识别“不可回答”的问题，避免在找不到答案时强行输出错误信息。对于开发者而言，该模型适合集成到知识库问答、智能客服等需要精准抽取答案的场景中，上手难度低，可通过 Hugging Face 等主流框架快速部署，是构建轻量级多语言 QA 系统的理想选择。

核心亮点

  • 支持多语言输入，能高效处理中文阅读理解
  • 支持 SQuAD 2.0，可识别并过滤无答案问题
  • 适用于知识库问答及精准文本片段抽取
  • 基于 RoBERTa 架构，推理速度快且部署便捷

使用方法

安装依赖
# 安装 Hugging Face transformers
pip install transformers torch
SDK 使用
# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("deepset/xlm-roberta-base-squad2")
tokenizer = AutoTokenizer.from_pretrained("deepset/xlm-roberta-base-squad2")

Hugging Face 下载

我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 huggingface_hub：

操作指引 
pip install -U huggingface_hub

命令行下载

下载完整模型库

下载完整模型库 
huggingface-cli download deepset/xlm-roberta-base-squad2

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例） 
huggingface-cli download deepset/xlm-roberta-base-squad2 config.json --local-dir ./dir

更多命令行下载选项，可参见官方文档

SDK 下载

SDK 下载 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('deepset/xlm-roberta-base-squad2')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://huggingface.co/deepset/xlm-roberta-base-squad2

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/deepset/xlm-roberta-base-squad2

模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。

PyTorch / Transformers 使用

安装 Transformers

安装 Transformers 
pip install -U transformers torch

模型加载和推理

模型加载和推理 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('deepset/xlm-roberta-base-squad2')
tokenizer = AutoTokenizer.from_pretrained('deepset/xlm-roberta-base-squad2')

模型下载

我们推荐使用命令行或者 ModelScope SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 ModelScope：

操作指引 
pip install modelscope

命令行下载

下载完整模型库

下载完整模型库 
modelscope download --model deepset/xlm-roberta-base-squad2

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例）

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例） 
modelscope download --model deepset/xlm-roberta-base-squad2 README.md --local_dir ./dir

更多更丰富的命令行下载选项，可参见具体文档

SDK 下载

SDK 下载 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('deepset/xlm-roberta-base-squad2')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://www.modelscope.cn/deepset/xlm-roberta-base-squad2.git

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/deepset/xlm-roberta-base-squad2.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook 快速开发

下载并安装 ModelScope library

下载并安装 ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

模型加载和推理

模型加载和推理 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'deepset/xlm-roberta-base-squad2')

完整文档

来源: HuggingFace

---
license: cc-by-4.0
datasets:

  • squad_v2

model-index:
  • name: deepset/xlm-roberta-base-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: 74.0354
name: Exact Match
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWMxNWQ2ODJkNWIzZGQwOWI4OTZjYjU3ZDVjZGQzMjI5MzljNjliZTY4Mzk4YTk4OTMzZWYxZjUxYmZhYTBhZSIsInZlcnNpb24iOjF9.eEeFYYJ30BfJDd-JYfI1kjlxJrRF6OFtj2GnkTCOO4kqX31inFy8ptDWusVlLFsUphm4dNWfTKXC5e-gytLBDA
- type: f1
value: 77.1833
name: F1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjg4MjNkOTA4Y2I5OGFlYTk1NWZjMWFlNjI5M2Y0NGZhMThhN2M4YmY2Y2RhZjcwYzU0MGNjN2RkZDljZmJmNiIsInZlcnNpb24iOjF9.TX42YMXpH4e0qu7cC4ARDlZWSkd55dwwyeyFXmOlXERNnEicDuFBCsy8WHLaqQCLUkzODJ22Hw4zhv81rwnlAQ
---

Multilingual XLM-RoBERTa base for Extractive QA on various languages

Overview

Language model: xlm-roberta-base Language: Multilingual Downstream-task: Extractive QA Training data: SQuAD 2.0 Eval data: SQuAD 2.0 dev set - German MLQA - German XQuAD Code: See an example extractive QA pipeline built with Haystack Infrastructure: 4x Tesla v100

Hyperparameters

code
batch_size = 22*4
n_epochs = 2
max_seq_len=256,
doc_stride=128,
learning_rate=2e-5,

Corresponding experiment logs in mlflow: link

Usage

In Haystack

Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack:
python
# After running pip install haystack-ai "transformers[torch,sentencepiece]"

from haystack import Document
from haystack.components.readers import ExtractiveReader


docs = [
    Document(content="Python is a popular programming language"),
    Document(content="python ist eine beliebte Programmiersprache"),
]


reader = ExtractiveReader(model="deepset/xlm-roberta-base-squad2")
reader.warm_up()


question = "What is a popular programming language?"
result = reader.run(query=question, documents=docs)
{'answers': [ExtractedAnswer(query='What is a popular programming language?', score=0.5740374326705933, data='python', document=Document(id=..., content: '...'), context=None, document_offset=ExtractedAnswer.Span(start=0, end=6),...)]}

For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.

In Transformers

python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "deepset/xlm-roberta-base-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)

Performance

Evaluated on the SQuAD 2.0 dev set with the official eval script.
code
"exact": 73.91560683904657
"f1": 77.14103746689592

Evaluated on German MLQA: test-context-de-question-de.json
"exact": 33.67279167589108
"f1": 44.34437105434842
"total": 4517

Evaluated on German XQuAD: xquad.de.json
"exact": 48.739495798319325
"f1": 62.552615701071495
"total": 1190

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

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