---
language: en
license: mit
library_name: transformers
datasets:
- glue
- sst2
metrics:
- accuracy
pipeline_tag: text-classification
widget:
- text: "This movie was an absolute masterpiece, I loved every minute of it!"
  example_title: "Positive Example"
- text: "The plot was boring and the acting was subpar."
  example_title: "Negative Example"
---

# BERT Base Uncased – Fine-tuned on SST-2

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the **GLUE SST-2** dataset for **binary sentiment analysis**.

---

## Model Details

- **Developed by:** Talip7  
- **Base model:** BERT Base Uncased  
- **Model type:** Transformer encoder (BERT)  
- **Language:** English  
- **Task:** Sentiment Analysis (Binary Classification)  

---

## Training Details

- **Dataset:** GLUE / SST-2  
- **Training framework:** PyTorch  
- **Libraries:** 🤗 Transformers, 🤗 Datasets, 🤗 Accelerate  
- **Optimizer:** AdamW  
- **Learning rate:** 3e-5  
- **Epochs:** 3  
- **Learning rate scheduler:** Linear  
- **Hardware:** GPU (via 🤗 Accelerate)

---

## Evaluation Results

The model was evaluated on the SST-2 validation set.

- **Accuracy:** **0.9289 (92.89%)**

---

## Intended Use

This model can be used for:

- Binary sentiment analysis on English text
- Educational purposes (learning fine-tuning with Hugging Face)
- Benchmarking sentiment classification models

---

## Limitations

- Trained only on movie reviews (SST-2); performance may degrade on other domains.
- Does not explicitly handle sarcasm or complex sentiment.
- Not suitable for multilingual sentiment analysis.

---

## Usage

### 🤗 Transformers Pipeline

```python
from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Talip7/bert-base-sst2-finetuned"
)

classifier("I love this project!")

```


### 🔥 PyTorch Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("Talip7/bert-base-sst2-finetuned")
model = AutoModelForSequenceClassification.from_pretrained("Talip7/bert-base-sst2-finetuned")

text = "This movie was absolutely fantastic!"

inputs = tokenizer(text, return_tensors="pt")

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

logits = outputs.logits
prediction = torch.argmax(logits, dim=-1).item()

label_map = {0: "Negative", 1: "Positive"}
print(f"Prediction: {label_map[prediction]}")