|
|
--- |
|
|
language: |
|
|
- en |
|
|
tags: |
|
|
- sentiment-analysis |
|
|
- text-classification |
|
|
- bert |
|
|
- manav |
|
|
- ManavDhayeCoder/sentiment-bert |
|
|
- ManavDhaye |
|
|
pipeline_tag: text-classification |
|
|
base_model: |
|
|
- google-bert/bert-base-uncased |
|
|
datasets: |
|
|
- imdb |
|
|
library_name: transformers |
|
|
widget: |
|
|
- text: This movie was amazing! |
|
|
- text: Worst movie I have ever seen. |
|
|
model-index: |
|
|
- name: sentiment-bert |
|
|
results: [] |
|
|
metrics: |
|
|
- accuracy |
|
|
--- |
|
|
|
|
|
# 📘 BERT Sentiment Analysis Model (Fine-Tuned on IMDB) |
|
|
|
|
|
This model is a fine-tuned version of **google-bert/bert-base-uncased**, trained on the **IMDB movie reviews dataset** for binary sentiment classification. |
|
|
|
|
|
It predicts whether text expresses **negative** or **positive** sentiment. |
|
|
|
|
|
This model is hosted by **[@ManavDhayeCoder](https://huggingface.co/ManavDhayeCoder)**. |
|
|
|
|
|
--- |
|
|
|
|
|
# 🚀 Model Overview |
|
|
|
|
|
| Property | Value | |
|
|
|----------|--------| |
|
|
| **Base model** | google-bert/bert-base-uncased | |
|
|
| **Task** | Sentiment Analysis (Sequence Classification) | |
|
|
| **Labels** | negative / positive | |
|
|
| **Dataset** | IMDB | |
|
|
| **Library** | Hugging Face Transformers | |
|
|
| **Format** | model.safetensors | |
|
|
|
|
|
The model has two classes: |
|
|
|
|
|
- `LABEL_0` → **negative** |
|
|
- `LABEL_1` → **positive** |
|
|
|
|
|
--- |
|
|
|
|
|
# 🔥 Quick Usage Example |
|
|
|
|
|
```python |
|
|
from transformers import pipeline |
|
|
|
|
|
clf = pipeline("text-classification", model="ManavDhayeCoder/sentiment-bert") |
|
|
|
|
|
print(clf("This movie was amazing!")) |
