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hello
chinmaygarde/hello
hello is a text classification model from chinmaygarde. Use it when you need a label for a piece of text. The card lists the license as apache-2.0.
Fine-tuned distilbert-base-uncased for binary sentiment classification (positive/negative) on a subset of the IMDB movie review dataset.
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At a glance
- Task
- Text Classification
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Mar 22, 2026
- Updated
- Mar 23, 2026
- SHA
- 9cacd9e8
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Model card
From the Hugging Face model README
DistilBERT Sentiment Classifier (IMDB)
Fine-tuned distilbert-base-uncased for binary sentiment classification (positive/negative) on a subset of the IMDB movie review dataset.
Model Details
- Base model: distilbert/distilbert-base-uncased
- Task: Sentiment analysis (binary classification)
- Labels:
0= negative,1= positive - Max sequence length: 128 tokens
Training
| Hyperparameter | Value |
|---|---|
| Dataset | IMDB (500 samples, 80/20 split) |
| Epochs | 2 |
| Batch size | 8 |
| Learning rate | 5e-5 (linear decay) |
Final eval loss: 0.0008
Usage
from transformers import pipeline
classifier = pipeline("text-classification", model="chinmaygarde/hello")
classifier("This movie was absolutely fantastic!")
# [{'label': 'LABEL_1', 'score': 0.999}]
- Task
- Text Classification
- Type
- distilbert
- License
- apache-2.0
- Languages
- en
- Created
- Mar 22, 2026
- Updated
- Mar 23, 2026