Back to all models
question-answering mask_token: [MASK]
Context
Query this model
馃敟 This model is currently loaded and running on the Inference API. 鈿狅笍 This model could not be loaded by the inference API. 鈿狅笍 This model can be loaded on the Inference API on-demand.
JSON Output
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."}' \
https://api-inference.huggingface.co/models/deepset/minilm-uncased-squad2
Share Copied link to clipboard

Monthly model downloads

deepset/minilm-uncased-squad2 deepset/minilm-uncased-squad2
532 downloads
last 30 days

pytorch

tf

Contributed by

deepset deepset.ai company
13 models

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

			
Copy to clipboard
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

Overview

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

Hyperparameters

seed=42
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
doc_stride=128
max_query_length=64
grad_acc_steps=4

Performance

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

Usage

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)

In FARM

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")

Authors

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

About us

deepset logo

We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems.

Some of our work:

Get in touch: Twitter | LinkedIn | Website