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
| 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` | |