Training was orchestrated end-to-end by The Founder — a personal ML agent that handles research, compute scheduling, experiment tracking, and artifact management autonomously using Kaggle (Tesla T4), Weights & Biases, and HuggingFace Hub.

Model Details

Property	Value
Base model	roberta-base
Fine-tuned on	dair-ai/emotion
Task	Emotion Classification
Epochs	4
Batch size	32
Learning rate	2e-05
GPU	Tesla T4 (Kaggle)
Train loss	0.2018
Eval loss	0.2680
Eval accuracy	0.9395
Duration	21.3 min

How to Get Started

python

from transformers import pipeline
clf = pipeline("text-classification", model="zanesmit29/founder-roberta-emotion-v1")
clf("Your input text here")

Uses

Direct Use

This model is suitable for Emotion Classification tasks in English. It can be used out-of-the-box with the Transformers pipeline API.

Out-of-Scope Use

This model was trained on a specific dataset and may not generalise to all domains or languages. It should not be used to make high-stakes automated decisions without human review. Performance on out-of-distribution data (e.g. non-English text, domain-specific jargon) is not guaranteed.

Training Details

Data

Trained on dair-ai/emotion — 4 epochs, no additional preprocessing beyond standard tokenization.

Hyperparameters

Hyperparameter	Value
Learning rate	2e-05
Batch size	32
Epochs	4
Optimizer	AdamW
LR scheduler	Linear with warmup
Max sequence length	128
fp16	true

Results

Metric	Value
Train loss	0.2018
Eval loss	0.2680
Eval accuracy	0.9395
Duration	21.3 min

Experiment Tracking

W&B Run

Infrastructure

Component	Tool
Compute	Kaggle (Tesla T4)
Experiment tracking	Weights & Biases
Artifact storage	HuggingFace Hub
Orchestration	The Founder