transformers pytorch bert question-answering generated_from_trainer dataset:squad base_model:eibakke/bert-finetuned-on-nq-short endpoints_compatible region:us
---
base_model: eibakke/bert-finetuned-on-nq-short
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-finetuned-nq-finetuned-squad
  results: []
---

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# bert-base-uncased-finetuned-nq-finetuned-squad

This model is a fine-tuned version of [eibakke/bert-finetuned-on-nq-short](https://huggingface.co/eibakke/bert-finetuned-on-nq-short) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2022

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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
- distributed_type: multi-GPU
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0087        | 1.0   | 5533  | 0.9909          |
| 0.7635        | 2.0   | 11066 | 1.0440          |
| 0.529         | 3.0   | 16599 | 1.2022          |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
