transformers pytorch bert question-answering generated_from_trainer dataset:squad_v2 base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 model-index endpoints_compatible region:us
---
base_model: bert-base-uncased
datasets:
- squad_v2
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-squad_v2
  results:
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad_v2
      type: squad_v2
      config: squad_v2
      split: validation
    metrics:
    - type: exact_match
      value: 71.692
      name: Exact Match
    - type: f1
      value: 75.4437
      name: F1
---

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# bert-base-uncased-finetuned-squad_v2

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. 


It achieves the following results on the evaluation set:
- Loss: 1.7075
- Exact Match: 71.6920
- F1-score: 75.4437


## Overview
**Language model:** bert-base-uncased  
**Language:** English  
**Downstream-task:** Extractive QA  
**Training data:** SQuAD 2.0  
**Eval data:** SQuAD 2.0  

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0593        | 1.0   | 8235  | 1.1296          |
| 0.7736        | 2.0   | 16470 | 1.1290          |
| 0.5682        | 3.0   | 24705 | 1.1725          |
| 0.4124        | 4.0   | 32940 | 1.4632          |
| 0.3137        | 5.0   | 41175 | 1.7075          |


### Framework versions

- Transformers 4.34.0
- Pytorch 1.12.1
- Datasets 2.14.5
- Tokenizers 0.14.1
