sneha21052004/emotion-roberta

sneha21052004 – Emotion Classification Model

This repository contains a fine-tuned text classification model for emotion recognition. It can classify input text into emotional categories (e.g., happy, sad, angry).

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Files in this Repository

File	Description
.gitattributes	Git configuration file.
config.json	Model configuration (architecture, number of labels, etc.).
merges.txt	BPE merge rules for tokenizer.
special_tokens_map.json	Maps special tokens (like <PAD>, <CLS>).
tokenizer.json	Complete tokenizer setup (vocab + merges).
tokenizer_config.json	Extra tokenizer settings (normalization, preprocessing).
vocab.txt	Tokenizer vocabulary.
pytorch_model.bin	Trained model weights (PyTorch). [Upload this if not already uploaded]

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Model Usage

You can use this model with the Hugging Face Transformers library:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("sneha21052004")
model = AutoModelForSequenceClassification.from_pretrained("sneha21052004")

# Example input
text = "I am feeling happy today!"
inputs = tokenizer(text, return_tensors="pt")

# Make prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.softmax(outputs.logits, dim=1)

print(predictions)