This model is intended to provide accurate answers to questions based on context passages. It can be used for a variety of tasks, including question-answering for search engines, chatbots, customer service systems, and other applications that require natural language understanding.

How to Use

python

from transformers import pipeline

question_answerer = pipeline("question-answering", model='VMware/minilmv2-l6-h384-from-bert-large-mrqa')

context = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT."

question = "What is MRQA?"

result = question_answerer(question=question, context=context)

print(result)

# {
#   'score': 0.30876627564430237,
#   'start': 30,
#   'end': 68,
#   'answer': 'Machine Reading for Question Answering'
# }

Training Details

The model was trained for 1 epoch on the MRQA training set.

Training Hyperparameters

python

args = TrainingArguments(
    "minilmv2-l6-h384-from-bert-large-mrqa",
    save_strategy="epoch",
    learning_rate=1e-5,
    num_train_epochs=1,
    weight_decay=0.01,
    per_device_train_batch_size=16,
)

Evaluation Metrics

The model was evaluated using standard metrics for question-answering models, including:

Exact match (EM): The percentage of questions for which the model produces an exact match with the ground truth answer.

F1 score: A weighted average of precision and recall, which measures the overlap between the predicted answer and the ground truth answer.

Model Family Performance

Parent Language Model	Number of Parameters	Training Time	Eval Time	Test Time	Eval EM	Eval F1	Test EM	Test F1
BERT-Tiny	4,369,666	26:11	0:41	0:04	22.78	32.42	10.18	18.72
BERT-Base	108,893,186	8:39:10	18:42	2:13	64.48	76.14	48.89	59.89
BERT-Large	334,094,338	28:35:38	1:00:56	7:14	69.52	80.50	55.00	65.78
DeBERTa-v3-Extra-Small	70,682,882	5:19:05	11:29	1:16	65.58	77.17	50.92	62.58
DeBERTa-v3-Base	183,833,090	12:13:41	28:18	3:09	71.43	82.59	59.49	70.46
DeBERTa-v3-Large	434,014,210	38:36:13	1:25:47	9:33	76.08	86.23	64.27	75.22
ELECTRA-Small	13,483,522	2:16:36	3:55	0:27	57.63	69.38	38.68	51.56
ELECTRA-Base	108,893,186	8:40:57	18:41	2:12	68.78	80.16	54.70	65.80
ELECTRA-Large	334,094,338	28:31:59	1:00:40	7:13	74.15	84.96	62.35	73.28
MiniLMv2-L6-H384-from-BERT-Large	22,566,146	2:12:48	4:23	0:40	59.31	71.09	41.78	53.30
MiniLMv2-L6-H768-from-BERT-Large	66,365,954	4:42:59	10:01	1:10	64.27	75.84	49.05	59.82
MiniLMv2-L6-H384-from-RoBERTa-Large	30,147,842	2:15:10	4:19	0:30	59.27	70.64	42.95	54.03
MiniLMv2-L12-H384-from-RoBERTa-Large	40,794,626	4:14:22	8:27	0:58	64.58	76.23	51.28	62.83
MiniLMv2-L6-H768-from-RoBERTa-Large	81,529,346	4:39:02	9:34	1:06	65.80	77.17	51.72	63.27
TinyRoBERTa	81,529.346	4:27:06*	9:54	1:04	69.38	80.07	53.29	64.16
RoBERTa-Base	124,056,578	8:50:29	18:59	2:11	69.06	80.08	55.53	66.49
RoBERTa-Large	354,312,194	29:16:06	1:01:10	7:04	74.08	84.38	62.20	72.88

* TinyRoBERTa's training time isn't directly comparable to the other models since it was distilled from VMware/roberta-large-mrqa that was already trained on MRQA.

Limitations and Bias

The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include:

Language: The model is designed to work with English text only and may not perform as well on other languages.
Domain-specific knowledge: The model has been trained on a general dataset and may not perform well on questions that require domain-specific knowledge.
Out-of-distribution questions: The model may struggle with questions that are outside the scope of the MRQA dataset. This is best demonstrated by the delta between its scores on the eval vs test datasets.

In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.