Sentiment Analysis with GoEmotions (RoBERTa + OpenVINO)

A fine-tuned RoBERTa-based model for detecting up to 28 different emotions in text, using the GoEmotions (simplified) dataset.
This repository contains:

• Training notebook (GoEmotions_Multiclass_Trainer.ipynb): shows how the model was trained with Intel XPU acceleration and Intel Extension for PyTorch.
• Inference notebook (GoEmotionsInference.ipynb): exports the model to ONNX, compiles it with OpenVINO, and demonstrates real-time inference.
• Model files:
  • goemotions_openvino_demo/checkpoints/best — best-performing checkpoint
  • goemotions_multilabel.onnx — optimized ONNX export for inference

Download Trained Models

Note: The ONNX and best model files are large (~476 MB each). They have been hosted on Google Drive—click to download and place into goemotions_openvino_demo/checkpoints/best.

File	Description	Google Drive Link
model.safetensors	Trained weights (PyTorch safetensors format)	model.safetensors
goemotions_multilabel.onnx	ONNX‐export of the same model	goemotions_multilabel.onnx

Requirements

Recommended Python version: 3.8–3.10

transformers>=4.30.0
datasets
numpy
torch
safetensors
openvino
jupyterlab

Live Demo

Give the model a spin in your browser—no install needed!
👉 SentIntel on Hugging Face Spaces

Build & Run Instructions

1. Clone the repository

git clone https://github.com/KhushiRajurkar/SentIntel.git
cd SentIntel

2. Set up Python environment

# Create and activate a virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Upgrade pip
pip install --upgrade pip

3. Install dependencies

pip install -r requirements.txt

4. Run the training notebook

1. Launch Jupyter Lab

jupyter lab

2. Open GoEmotions_Multiclass_Trainer.ipynb

3. Adjust hyperparameters if needed and execute all cells to fine-tune your model.

5. Download prebuilt model files

1. Download the following and place them under the paths shown:

   SentIntel/
   └── goemotions_openvino_demo/
       └── checkpoints/
           └── best/
               ├── model.safetensors
               └── goemotions_multilabel.onnx
   

6. Export & convert for inference

1. Load the best checkpoint from goemotions_openvino_demo/checkpoints/best/ and rebuild the model.

2. Export to ONNX by running the ONNX export cell.

3. Compile with OpenVINO (IR → compiled model) by running the Model Optimizer & compile cells.

7. Run inference demo locally

from transformers import AutoTokenizer
from openvino.runtime import Core
import numpy as np

# 1) Load tokenizer + compiled OpenVINO IR
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
core      = Core()
compiled  = core.compile_model("goemotions_multilabel.xml", device_name="CPU")

# 2) Prepare inputs
texts  = ["I am so happy!", "This is bad..."]
tokens = tokenizer(texts,
                   padding="max_length",
                   truncation=True,
                   max_length=128,
                   return_tensors="np")

# 3) Inference
outs   = compiled([tokens["input_ids"], tokens["attention_mask"]])
logits = outs[compiled.output(0)]
probs  = 1 / (1 + np.exp(-logits))

# 4) Threshold & map back to human-readable emotions
THRESHOLD = 0.3
preds     = (probs > THRESHOLD).astype(int)

emotion_labels = [
  "admiration","amusement","anger","annoyance","approval","caring",
  "confusion","curiosity","desire","disappointment","disapproval",
  "disgust","embarrassment","excitement","fear","gratitude","grief",
  "joy","love","nervousness","optimism","pride","realization","relief",
  "remorse","sadness","surprise","neutral"
]

for i, text in enumerate(texts):
    fired = [emotion_labels[j] for j, v in enumerate(preds[i]) if v]
    print(f"{text!r} → {fired}")