---
language: en
license: mit
library_name: transformers
tags:
- sentiment-analysis
- classification
- from-scratch
- multi-domain
datasets:
- imdb
- glue
metrics:
- accuracy
model-index:
- name: VibeCheck-v1
  results:
  - task:
      type: text-classification
      name: Sentiment Analysis
    dataset:
      name: Mixed (IMDb & SST2)
      type: multi-domain
    metrics:
    - type: accuracy
      value: [INSERT_ACCURACY_HERE] # Replace with your final validation accuracy
pipeline_tag: text-classification
---

# VibeCheck v1

VibeCheck v1 is a high-performance, multi-domain Transformer model trained **entirely from scratch**. Unlike its predecessor, this model was trained on a balanced mix of long-form reviews and short-form conversational data, making it a versatile tool for analyzing "vibes" across different types of English text.

## Model Description
- **Architecture:** Enhanced Custom Transformer (DistilBERT-style)
- **Parameters:** ~11.17 Million
- **Layers:** 4 (Increased depth for better abstraction)
- **Attention Heads:** 8
- **Hidden Dimension:** 256 (Hidden Feed-Forward: 1024)
- **Training Data:** ~92,349 samples (Mixed IMDb Movie Reviews & SST-2 Sentence Bank)
- **Training Duration:** ~25-30 minutes on NVIDIA T4 GPU

## Capabilities
- **Multi-Domain Versatility:** Reliable on everything from formal emails to short chat messages.
- **Enhanced Context Awareness:** 4 layers of self-attention allow for a deeper understanding of sentence structure.
- **Linguistic Nuance:** Strong performance on complex negatives (e.g., "not as bad as I thought") and rhetorical questions.
- **Robustness:** High tolerance for slang, typos, and non-standard English.

## Limitations
- **Language Focus:** Primarily trained on English. While it shows some intuition for other languages, accuracy may vary.
- **Binary Nature:** Strictly classifies text as Positive or Negative; it does not detect neutral intent or specific emotions (like anger or joy).

## How to use (Inference Script)
To use this model, download the `VibeCheck_v1_Model.zip`, unpack it, and run the provided `inference.py` script. Make sure to point the script to the unpacked directory.

## Examples (VibeCheck v1 in Action)

### Example 1: Formal Business Email
**Input:**
```plaintext
Dear Team, I am writing to express my deep disappointment regarding the recent project update. The quality is subpar.
```

**Output:** NEGATIVE | Confidence: 97.60%

### Example 2: Short Conversational Fragment
**Input:**

```plaintext
That sounds like a fantastic plan! I'm starving.
```

**Output:** POSITIVE | Confidence: 74.15%

### Example 3: Sarcastic Observation of a movie review
**Input:**

```plaintext
Wow! What an amazing view we have out of this window!
```

**Output:** POSITIVE | Confidence: 99.43%

### Example 4: Classic Test
**Input:**

```plaintext
You are dumb.
```

**Output:** NEGATIVE | Confidence: 87.85%

### Example 5: Simple chat
**Input:**

```plaintext
Did you see the new movie?' B: 'Yeah, it was okay, but the ending felt a bit rushed.' A: 'I totally agree, it could have been better.'
```

**Output:** NEGATIVE | Confidence: 80.98%

## Training code
The full training code for this multi-domain version is available in `train.ipynb`.