README.md

---
license: mit
datasets:
- shhossain/book-text-classifier
language:
- en
pipeline_tag: text-classification
widget:
- text: "Shen Yuanye didn’t expect to go back to Huang Ni’s matter again, this matter of being killed without finding the murderer, who else could be beside her."
  example_title: "Book Text"
- text: "I am so sorry this is a day late, guys. Unfortunately, my internet was down so it was out of my control. Its still intermittent but hopefully it will be fine by next week."
  example_title: "Normal Text"
metrics:
- accuracy
model-index:
- name: shhossain/bert-tiny-book-text-classifier
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      type: shhossain/book-text-classifier
      name: book-text-classifier
      split: test        
    metrics:
      - type: accuracy
        value: 0.999128  
---
# Book Test Classifier

Classify book text (mostly fictional book)


## Model Details

### Model Description

This model is finetuned on bert-tiny for classifying book text.



- **Developed by:** [shhossain](https://github.com/shhossain)
- **Model type:** [Bert]
- **Language(s) (NLP):** [English]
- **License:** [MIT]
- **Finetuned from model [Bert-Tiny]:** [bert-tiny](https://huggingface.co/prajjwal1/bert-tiny)



## Uses


```python
from transformers import pipeline, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('prajjwal1/bert-tiny')

pipe = pipeline('text-classification', model='shhossain/bert-tiny-book-text-classifier')

book_text = """Shen Yuanye didn’t expect to go back to Huang Ni’s matter again, this matter of being killed without finding the murderer, who else could be beside her."""

pipe(book_text) # LABEL_1
>> [{'label': 'LABEL_1', 'score': 0.9998537302017212}]

normal_text = """I am so sorry this is a day late, guys. Unfortunately, my internet was down so it was out of my control. Its still intermittent but hopefully it will be fine by next week. Hopefully the fact that Skye and Pietro are back in form will help make up for it."""

pipe(normal_text) # LABEL_0
>> [{'label': 'LABEL_0', 'score': 0.9984021782875061}]
```