---
license: mit
metrics:
- accuracy
base_model:
- google-bert/bert-base-uncased
datasets:
- shahxeebhassan/human_vs_ai_sentences
pipeline_tag: text-classification
library_name: transformers
---

## Model Description
This model is a fine-tuned BERT model for AI content detection.

## Training Data
The model was trained on a [<span style="color: blue;">dataset</span>
](https://huggingface.co/datasets/shahxeebhassan/human_vs_ai_sentences) of over 100,000 sentences, each labeled as either AI-generated or human-written. This approach allows the model to predict the nature of each individual sentence, which is particularly useful for highlighting AI-written content within larger texts.

## Evaluation Metrics
The model achieved an accuracy of 90% on the validation & test set.

## Usage
```python
import torch
from transformers import BertTokenizer, BertForSequenceClassification

tokenizer = BertTokenizer.from_pretrained("shahxeebhassan/bert_base_ai_content_detector")
model = BertForSequenceClassification.from_pretrained("shahxeebhassan/bert_base_ai_content_detector")

inputs = tokenizer("Distance learning will not benefit students because the students are not able to develop as good of a relationship with their teachers.", return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits

probabilities = torch.softmax(logits, dim=1).cpu().numpy()

predicted_label = probabilities.argmax(axis=1)

print(f"Predicted label for the input text: {predicted_label[0]}")