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bert-squad-qa-full-finetuning

bert-base-uncased fully fine-tuned (BertForQuestionAnswering, all layers trained) for extractive question answering on 15,000 examples of SQuAD v1.1.

The feature-based baseline (frozen BERT + Logistic Regression) is published separately at Giobbva/bert-squad-qa-feature-based.

Model	Exact Match	F1
Frozen BERT + Logistic Regression (bert-squad-qa-feature-based)	14.35	24.57
This model (full fine-tuning)	69.04	79.53

Evaluated on the full SQuAD v1.1 validation set (10,570 questions).

Files in this repository

BertForQuestionAnswering weights, config and tokenizer (best epoch by validation loss).

Training

AdamW, encoder lr 2e-05, QA head lr 0.001, weight decay 0.01, linear schedule with 10% warm-up
2 epochs, batch size 16, max length 384, stride 128, fp16=True, seed 42

Usage

from transformers import pipeline
qa = pipeline("question-answering", model="Giobbva/bert-squad-qa-full-finetuning")
qa(question="Where is the Eiffel Tower?", context="The Eiffel Tower is in Paris.")

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Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for Giobbva/bert-squad-qa-full-finetuning

Base model

google-bert/bert-base-uncased

Finetuned

(6996)

this model

Dataset used to train Giobbva/bert-squad-qa-full-finetuning

Evaluation results

exact_match on SQuAD v1.1
validation set self-reported

69.040
f1 on SQuAD v1.1
validation set self-reported

79.530