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question-answering mask_token: [MASK]
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API endpoint
								curl -X POST \
-H "Authorization: Bearer YOUR_ORG_OR_USER_API_TOKEN" \
-H "Content-Type: application/json" \
-d '{"question": "Where does she live?", "context": "She lives in Berlin."}' \
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deepset/minilm-uncased-squad2 deepset/minilm-uncased-squad2
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Contributed by

deepset company
13 models

How to use this model directly from the 馃/transformers library:

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from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/minilm-uncased-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deepset/minilm-uncased-squad2")

MiniLM-L12-H384-uncased for QA


Language model: microsoft/MiniLM-L12-H384-uncased Language: English Downstream-task: Extractive QA Training data: SQuAD 2.0 Eval data: SQuAD 2.0 Code: See example in FARM Infrastructure: 1x Tesla v100


batch_size = 12
n_epochs = 4
base_LM_model = "microsoft/MiniLM-L12-H384-uncased"
max_seq_len = 384
learning_rate = 4e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2


Evaluated on the SQuAD 2.0 dev set with the official eval script.

"exact": 76.13071675229513,
"f1": 79.49786500219953,
"total": 11873,
"HasAns_exact": 78.35695006747639,
"HasAns_f1": 85.10090269418276,
"HasAns_total": 5928,
"NoAns_exact": 73.91084945332211,
"NoAns_f1": 73.91084945332211,
"NoAns_total": 5945


In Transformers

from transformers.pipelines import pipeline
from transformers.modeling_auto import AutoModelForQuestionAnswering
from transformers.tokenization_auto import AutoTokenizer

model_name = "deepset/minilm-uncased-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)


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

model_name = "deepset/minilm-uncased-squad2"

# a) Get predictions
nlp = Inferencer.load(model_name, task_type="question_answering")
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)

# b) Load model & tokenizer
model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering")
tokenizer = Tokenizer.load(model_name)

In haystack

For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack:

reader = FARMReader(model_name_or_path="deepset/minilm-uncased-squad2")
# or
reader = TransformersReader(model="deepset/minilm-uncased-squad2",tokenizer="deepset/minilm-uncased-squad2")


Vaishali Pal vaishali.pal [at] Branden Chan: branden.chan [at] Timo M枚ller: timo.moeller [at] Malte Pietsch: malte.pietsch [at] Tanay Soni: tanay.soni [at]

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