Downloads · 30 days
11
24% of all-time downloads
Bert_fine_tuned_eq
sreenathsree1578/Bert_fine_tuned_eq
Bert_fine_tuned_eq is a text classification model from sreenathsree1578. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
A fine-tuned DistilBERT model for detecting emotional intelligence levels in workplace-focused text data.
All-time downloads
46
Public
Parameters
67M
268 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors268 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- distilbert
- Access
- Public
- Created
- Jan 14, 2026
- Updated
- Jan 14, 2026
- SHA
- fae986d4
Try a prompt
Model card
From the Hugging Face model README
EQ Detection Model
A fine-tuned DistilBERT model for detecting emotional intelligence levels in workplace-focused text data.
Model Description
- Task: Text Classification
- Model Type: Emotional Intelligence (EQ) Detection
- Base Model: distilbert-base-uncased
- Language: English
- Output Classes: 3 (NEGATIVE / NEUTRAL / POSITIVE)
- Training Dataset Size: 2,796 workplace communication samples
The model is designed to evaluate emotional regulation, tone, and behavioral intelligence in professional communication.
Label Schema
| Label | ID | Description |
|---|---|---|
| NEGATIVE | 0 | Poor emotional regulation, negative or aggressive expressions |
| NEUTRAL | 1 | Emotionally neutral or factual statements |
| POSITIVE | 2 | High emotional intelligence and constructive emotional behavior |
Training Performance
| Epoch | Training Loss | Validation Loss | Accuracy |
|---|---|---|---|
| 1 | 0.188500 | 0.147850 | 94.89% |
| 2 | 0.055100 | 0.120229 | 96.39% |
Final Validation Accuracy: 96.39%
Training Configuration
- Framework: Hugging Face Transformers
- Optimizer: AdamW
- Batch Size: 16
- Learning Rate: 2e-5
- Epochs: 2
- Max Sequence Length: 128 tokens
Intended Use
This model is intended for:
- Workplace communication analysis
- Emotional intelligence assessment
- HR analytics and employee development
- Team interaction and behavioral insights
How to Use
Load the Model
from transformers import pipeline
classifier = pipeline(
"text-classification",
model="sreenathsree1578/Eq_funetuned"
)
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- mit
- Languages
- en
- Created
- Jan 14, 2026
- Updated
- Jan 14, 2026