Text Classification
Transformers
PyTorch
Safetensors
English
roberta
sentiment-analysis
huggingface
PyTorch
Instructions to use hasanmustafa0503/SentimentModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hasanmustafa0503/SentimentModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hasanmustafa0503/SentimentModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hasanmustafa0503/SentimentModel") model = AutoModelForSequenceClassification.from_pretrained("hasanmustafa0503/SentimentModel") - Notebooks
- Google Colab
- Kaggle
File size: 624 Bytes
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language: en
license: apache-2.0
pipeline_tag: text-classification
tags:
- sentiment-analysis
- roberta
- text-classification
- huggingface
- transformers
- PyTorch
---
# 🧠 Sentiment Analysis Model
This model performs binary sentiment classification (Positive/Negative) on user-provided text inputs. It is trained to assist in mental health-related sentiment detection.
## 🚀 Usage
You can try this model via the Hugging Face Inference API or integrate it in your application using the `transformers` library.
## 🧪 Example
**Input:**
"I feel really hopeful today!"
**Output:**
`Positive`
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