---
library_name: transformers
tags:
- tone
datasets:
- Dc-4nderson/tone_dataset2
language:
- en
metrics:
- accuracy
- f1
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: text-classification
---

DistilBERT Tone Classification Model 

This model fine-tunes distilbert-base-uncased to classify tone into 7 categories relevant to community and mentorship transcripts.

📌 Labels

uplifting

thoughtful

practical

reflective

motivational

informative

optimistic

📊 Dataset

The model is trained on the tone-dataset
,
a dataset containing 1000+ labeled examples created for the MyVillageProject tone classification task.
Data includes first-person and third-person statements, anecdotes, factual notes, and reflective entries.

🚀 Training

Base model: distilbert-base-uncased

Optimizer: AdamW (lr=2e-5)

Batch size: 16

Epochs: 8

Loss: CrossEntropy

Metrics: Accuracy + Weighted F1

📈 Validation Metrics
Epoch	Training Loss	Validation Loss  Accuracy	F1
1	          No log	   1.281651	    0.782288	0.778880
2	          No log	   0.779447	    0.845018	0.843397
3	          No log	   0.566092	    0.859779	0.856186
4	          No log	   0.415437	    0.892989	0.892445
5	          No log	   0.340598	    0.915129	0.914765
6             0.729500     0.307513	    0.922509	0.922262
7	          0.729500	   0.296827	    0.915129	0.915210
8	          0.729500	   0.285301	    0.922509    0.922262

Final Training Summary:

TrainOutput(global_step=704,
training_loss=0.5666945034807379,
metrics={'train_runtime': 42.6317,
'train_samples_per_second': 261.402,
'train_steps_per_second': 16.514,
'total_flos': 369087080441856.0, 
'train_loss': 0.5666945034807379, 
'epoch': 8.0})

💻 Usage
from transformers import pipeline

classifier = pipeline("text-classification", model="Dc-4nderson/tone-distilbert")

text = "Ronnie mentioned the turnout was twice what they expected, and it felt like a victory."
print(classifier(text))


Output:

[{'label': 'uplifting'}]

👥 Maintainer

Dequan Anderson/ Dc-4nderson