Update README.md

README.md

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----
-license: mit
----
+---
+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_vibecheck.ipynb`.