distilbert_emotions_fellowship

Distilbert emotions fellowship

IA-Fellowship is an innovative application designed to accurately recognize emotions and stress levels embedded within textual data. By harnessing cutting-edge natural language processing (NLP) and machine learning techniques, it facilitates the identification and analysis of various human emotional states conveyed through text.

Training

Dataset and Objectives: The model was trained on a dataset comprising nearly 500,000 records annotated with six emotion labels: sadness (0), joy (1), love (2), anger (3), fear (4), and surprise (5). The dataset was sourced from Twitter records and is publicly available on Kaggle. Training involved a split ratio of 20/80%. Benchmark

Results or Performance Metrics: Discuss the model's performance on a test or validation dataset. Review the detailed benchmarks and performance analysis provided in this Google Colab Notebook.

How to Use

Provide detailed instructions on how to use your model. For instance:

from transformers import AutoTokenizer, AutoModel



tokenizer = AutoTokenizer.from_pretrained("Valwolfor/distilbert_emotions_fellowship")
model = AutoModel.from_pretrained("Valwolfor/distilbert_emotions_fellowship")

# Sample text to test the model
text = "Insert your text here to test the model"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

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license: mit