---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: bert-base-uncased-finetuned-squad2
  results: []
pipeline_tag: question-answering
metrics:
- exact_match
- f1
language:
- en
---

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# bert-base-uncased-finetuned-squad2

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3537

## Model description

BERTbase fine-tuned on SQuAD 2.0 : Encoder-based Transformer Language model, pretrained with Masked Language Modeling and Next Sentence Prediction.
Suitable for Question-Answering tasks, predicts answer spans within the context provided. 

Training data: Train-set SQuAD2.0
Evaluation data: Validation-set SQuAD2.0
Hardware Accelerator used: GPU Tesla T4

## Intended uses & limitations

For Question-Answering -

question = "How many programming languages does BLOOM support?"
context = "BLOOM has 176 billion parameters and can generate text in 46 languages natural languages and 13 programming languages."

from transformers import pipeline

question_answerer = pipeline("question-answering", model="IProject-10/bert-base-uncased-finetuned-squad2")
question_answerer(question=question, context=context)

{{ direct_use | default("[question-answering]", true)}}
{{ downstream_use | default("[question-answering]", true)}}

## Results

Evaluation on SQuAD 2.0 validation dataset:




### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-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: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0122        | 1.0   | 8235  | 1.0740          |
| 0.6805        | 2.0   | 16470 | 1.0820          |
| 0.4542        | 3.0   | 24705 | 1.3537          |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3