tensorboard safetensors bert multiple-choice generated_from_trainer question-answering dataset:allenai/swag base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 region:us
---
base_model: google-bert/bert-base-uncased
datasets:
- allenai/swag
license: apache-2.0
metrics:
- accuracy
pipeline_tag: question-answering
tags:
- multiple-choice
- generated_from_trainer
model-index:
- name: bert-finetuned-swag
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Model Card: BERT Fine-tuned on SWAG

## Model Overview

This model is a fine-tuned version of [BERT-Base, Uncased](https://huggingface.co/google-bert/bert-base-uncased) developed by Google. The fine-tuning was performed on the SWAG dataset, a large-scale dataset for grounded commonsense inference, though the specific details of the dataset used were not provided. The model was fine-tuned for a single epoch and is optimized for tasks related to natural language understanding, particularly in scenarios requiring reasoning about the world using commonsense.

### Model Architecture

- **Base Model**: BERT-Base, Uncased
- **Layers**: 12 Transformer layers
- **Parameters**: 110M
- **Pre-training**: The base model was pre-trained on the English Wikipedia and BookCorpus datasets.

## Performance

The model achieved the following results on the evaluation set:

- **Validation Loss**: 0.5240
- **Accuracy**: 79.70%

## Intended Use

### Use Cases

This model is intended for tasks requiring natural language understanding, especially those involving commonsense reasoning. Potential use cases include:

- Multiple-choice question answering
- Contextual word embedding generation
- Commonsense inference tasks

### Limitations

- **Data Bias**: As the dataset specifics are unknown, there might be biases in the training data that could affect the model’s predictions.
- **Generalization**: The model's performance on domains outside of commonsense reasoning tasks (like domain-specific text) may be suboptimal.
- **Ethical Considerations**: Users should be aware of potential ethical concerns when applying this model to sensitive or critical tasks. Misinterpretation of commonsense reasoning could lead to flawed or biased outcomes.

## Training and Evaluation Data

### Dataset

The model was fine-tuned on a dataset intended for grounded commonsense inference, likely the SWAG dataset. The specifics of the dataset, including size, distribution, and preprocessing methods, were not provided.

## Training Procedure

### Hyperparameters

The model was trained using the following hyperparameters:

- **Learning Rate**: 5e-05
- **Train Batch Size**: 16
- **Eval Batch Size**: 16
- **Optimizer**: Adam (betas: (0.9, 0.999), epsilon: 1e-08)
- **Learning Rate Scheduler**: Linear
- **Number of Epochs**: 1
- **Seed**: 42

### Training Results

The training and evaluation results are summarized below:

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6971        | 1.0   | 4597 | 0.5240          | 0.7970   |

### Framework Versions

The following software versions were used during training:

- **Transformers**: 4.42.4
- **PyTorch**: 2.4.0+cu121
- **Datasets**: 2.21.0
- **Tokenizers**: 0.19.1

## Ethical Considerations

When deploying this model, users should be cautious of potential biases and limitations inherent in the dataset and the model’s training process. Ensuring that the model is used in a manner that is fair, unbiased, and ethical is crucial, particularly in sensitive applications.

## Contact Information

For further information or questions, please contact the maintainers of this model or refer to the associated documentation and code repository.