README.md
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1 ---
2 language: multilingual
3 license: cc-by-4.0
4 tags:
5 - question-answering
6 datasets:
7 - squad_v2
8 model-index:
9 - name: deepset/xlm-roberta-large-squad2
10 results:
11 - task:
12 type: question-answering
13 name: Question Answering
14 dataset:
15 name: squad_v2
16 type: squad_v2
17 config: squad_v2
18 split: validation
19 metrics:
20 - type: exact_match
21 value: 81.8281
22 name: Exact Match
23 verified: true
24 verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzVhZDE2NTg5NmUwOWRkMmI2MGUxYjFlZjIzNmMyNDQ2MDY2MDNhYzE0ZjY5YTkyY2U4ODc3ODFiZjQxZWQ2YSIsInZlcnNpb24iOjF9.f_rN3WPMAdv-OBPz0T7N7lOxYz9f1nEr_P-vwKhi3jNdRKp_JTy18MYR9eyJM2riKHC6_ge-8XwfyrUf51DSDA
25 - type: f1
26 value: 84.8886
27 name: F1
28 verified: true
29 verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGE5MWJmZGUxMGMwNWFhYzVhZjQwZGEwOWQ4N2Q2Yjg5NzdjNDFiNDhiYTQ1Y2E5ZWJkOTFhYmI1Y2Q2ZGYwOCIsInZlcnNpb24iOjF9.TIdH-tOx3kEMDs5wK1r6iwZqqSjNGlBrpawrsE917j1F3UFJVnQ7wJwaj0OIgmC4iw8OQeLZL56ucBcLApa-AQ
30 ---
31
32 # Multilingual XLM-RoBERTa large for Extractive QA on various languages
33
34 ## Overview
35 **Language model:** xlm-roberta-large
36 **Language:** Multilingual
37 **Downstream-task:** Extractive QA
38 **Training data:** SQuAD 2.0
39 **Eval data:** SQuAD dev set - German MLQA - German XQuAD
40 **Training run:** [MLFlow link](https://public-mlflow.deepset.ai/#/experiments/124/runs/3a540e3f3ecf4dd98eae8fc6d457ff20)
41 **Code:** See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline)
42 **Infrastructure**: 4x Tesla v100
43
44 ## Hyperparameters
45
46 ```
47 batch_size = 32
48 n_epochs = 3
49 base_LM_model = "xlm-roberta-large"
50 max_seq_len = 256
51 learning_rate = 1e-5
52 lr_schedule = LinearWarmup
53 warmup_proportion = 0.2
54 doc_stride=128
55 max_query_length=64
56 ```
57
58 ## Usage
59
60 ### In Haystack
61 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.
62 To load and run the model with [Haystack](https://github.com/deepset-ai/haystack/):
63 ```python
64 # After running pip install haystack-ai "transformers[torch,sentencepiece]"
65
66 from haystack import Document
67 from haystack.components.readers import ExtractiveReader
68
69 docs = [
70 Document(content="Python is a popular programming language"),
71 Document(content="python ist eine beliebte Programmiersprache"),
72 ]
73
74 reader = ExtractiveReader(model="deepset/xlm-roberta-large-squad2")
75 reader.warm_up()
76
77 question = "What is a popular programming language?"
78 result = reader.run(query=question, documents=docs)
79 # {'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),...)]}
80 ```
81 For a complete example with an extractive question answering pipeline that scales over many documents, check out the [corresponding Haystack tutorial](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline).
82
83 ### In Transformers
84 ```python
85 from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
86
87 model_name = "deepset/xlm-roberta-large-squad2"
88
89 # a) Get predictions
90 nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
91 QA_input = {
92 'question': 'Why is model conversion important?',
93 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
94 }
95 res = nlp(QA_input)
96
97 # b) Load model & tokenizer
98 model = AutoModelForQuestionAnswering.from_pretrained(model_name)
99 tokenizer = AutoTokenizer.from_pretrained(model_name)
100 ```
101
102 ## Performance
103 Evaluated on the SQuAD 2.0 English dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
104 ```
105 "exact": 79.45759285774446,
106 "f1": 83.79259828925511,
107 "total": 11873,
108 "HasAns_exact": 71.96356275303644,
109 "HasAns_f1": 80.6460053117963,
110 "HasAns_total": 5928,
111 "NoAns_exact": 86.93019343986543,
112 "NoAns_f1": 86.93019343986543,
113 "NoAns_total": 5945
114 ```
115
116 Evaluated on German [MLQA: test-context-de-question-de.json](https://github.com/facebookresearch/MLQA)
117 ```
118 "exact": 49.34691166703564,
119 "f1": 66.15582561674236,
120 "total": 4517,
121 ```
122
123 Evaluated on German [XQuAD: xquad.de.json](https://github.com/deepmind/xquad)
124 ```
125 "exact": 61.51260504201681,
126 "f1": 78.80206098332569,
127 "total": 1190,
128 ```
129
130 ## Usage
131
132 ### In Haystack
133 For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/):
134 ```python
135 reader = FARMReader(model_name_or_path="deepset/xlm-roberta-large-squad2")
136 # or
137 reader = TransformersReader(model="deepset/xlm-roberta-large-squad2",tokenizer="deepset/xlm-roberta-large-squad2")
138 ```
139
140 ### In Transformers
141 ```python
142 from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
143
144 model_name = "deepset/xlm-roberta-large-squad2"
145
146 # a) Get predictions
147 nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
148 QA_input = {
149 'question': 'Why is model conversion important?',
150 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
151 }
152 res = nlp(QA_input)
153
154 # b) Load model & tokenizer
155 model = AutoModelForQuestionAnswering.from_pretrained(model_name)
156 tokenizer = AutoTokenizer.from_pretrained(model_name)
157 ```
158
159 ## Authors
160 **Branden Chan:** branden.chan@deepset.ai
161 **Timo Möller:** timo.moeller@deepset.ai
162 **Malte Pietsch:** malte.pietsch@deepset.ai
163 **Tanay Soni:** tanay.soni@deepset.ai
164
165 ## About us
166
167 <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
168 <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
169 <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/>
170 </div>
171 <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
172 <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/>
173 </div>
174 </div>
175
176 [deepset](http://deepset.ai/) is the company behind the production-ready open-source AI framework [Haystack](https://haystack.deepset.ai/).
177
178 Some of our other work:
179 - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")](https://huggingface.co/deepset/tinyroberta-squad2)
180 - [German BERT](https://deepset.ai/german-bert), [GermanQuAD and GermanDPR](https://deepset.ai/germanquad), [German embedding model](https://huggingface.co/mixedbread-ai/deepset-mxbai-embed-de-large-v1)
181 - [deepset Cloud](https://www.deepset.ai/deepset-cloud-product), [deepset Studio](https://www.deepset.ai/deepset-studio)
182
183 ## Get in touch and join the Haystack community
184
185 <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>.
186
187 We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p>
188
189 [Twitter](https://twitter.com/Haystack_AI) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://haystack.deepset.ai/) | [YouTube](https://www.youtube.com/@deepset_ai)
190
191 By the way: [we're hiring!](http://www.deepset.ai/jobs)