Go-Raw/final-sentiment-model-go-raw

Final Sentiment Model - Go-Raw

Model description

This is a fine-tuned roberta-base model for multi-class sentiment classification.
It was trained on a custom dataset of ~240k examples with 3 sentiment classes:

• 0: Negative
• 1: Positive
• 2: Neutral

The model shows significant improvement over the base model on this task.

Intended uses & limitations

• ✅ Suitable for English text sentiment analysis.
• 🚫 Not tested on other languages or domains beyond training data.
• 🚫 Not suitable for detecting abusive, toxic, or hate speech.

Training details

• Base model: roberta-base
• Epochs: 3
• Learning rate: 2e-5
• Batch size: 8
• Optimizer: AdamW

Evaluation

Dataset

• Train set: 1,94,038 examples
• Test set: 48,510 examples

Performance

Metric	Base Model	Fine-tuned Model
Accuracy	34.1%	88.1%
Macro F1	24.3%	87.5%
Weighted F1	27.1%	88.1%

Per-class metrics

Class	Precision	Recall	F1-score
0 (Negative)	85.3%	83.1%	84.2%
1 (Neutral)	91.4%	89.8%	90.5%
2 (Positive)	86.0%	89.4%	87.7%

How to use

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Go-Raw/final-sentiment-model-go-raw")
tokenizer = AutoTokenizer.from_pretrained("Go-Raw/final-sentiment-model-go-raw")

text = "I absolutely love this!"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
predicted_class = outputs.logits.argmax().item()
print(predicted_class)