File size: 1,390 Bytes
8cbd529 f5ede23 8cbd529 f5ede23 8cbd529 f5ede23 8cbd529 f5ede23 ea91484 8cbd529 ea91484 8cbd529 ea91484 f5ede23 8cbd529 ea91484 8cbd529 f5ede23 8cbd529 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
---
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!")) |