models deepset roberta base squad2 - Azure/azureml-assets GitHub Wiki

deepset-roberta-base-squad2

Overview

This is the roberta-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.

Training Details

Hyperparameters

batch_size = 96
n_epochs = 2
base_LM_model = "roberta-base"
max_seq_len = 386
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride=128
max_query_length=64

Evaluation Results

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

"exact": 79.87029394424324,
"f1": 82.91251169582613,

"total": 11873,
"HasAns_exact": 77.93522267206478,
"HasAns_f1": 84.02838248389763,
"HasAns_total": 5928,
"NoAns_exact": 81.79983179142137,
"NoAns_f1": 81.79983179142137,
"NoAns_total": 5945

Model Evaluation samples

Task Use case Dataset Python sample (Notebook) CLI with YAML
Question Answering Extractive Q&A Squad v2 evaluate-model-question-answering.ipynb evaluate-model-question-answering.yml

Inference samples

Inference type Python sample (Notebook)
Real time sdk-example.ipynb
Real time question-answering-online-endpoint.ipynb

Sample inputs and outputs

Sample input

{
    "input_data": {
        "question": "What's my name?",
        "context": "My name is John and I live in Seattle"
    }
}

Sample output

[
  "John"
]

Version: 17

Tags

model_specific_defaults : {'apply_deepspeed': 'true', 'apply_lora': 'true', 'apply_ort': 'true'}

View in Studio: https://ml.azure.com/registries/azureml/models/deepset-roberta-base-squad2/version/17

Properties

SHA: e84d19c1ab20d7a5c15407f6954cef5c25d7a261

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