Text Classification
Transformers
Safetensors
English
roberta
sentiment-analysis
custom-dataset
text-embeddings-inference
Instructions to use raghavv2710/sentiment-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raghavv2710/sentiment-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raghavv2710/sentiment-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("raghavv2710/sentiment-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("raghavv2710/sentiment-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Verified signatureπ Production-Ready This model works out of the box with: β FastAPI backends β Hugging Face pipeline β Gradio / Streamlit frontends β Real-time inference on CPU/GPU Let me know if you'd like help with a live demo or deployment script. π€ Let's Connect Crafted with care by <span style="color:#48b2f7; font-weight:600;">Raghavendra</span> π οΈ If this model helps your project, please β it, give feedback, or open issues! Fork it to adapt it for your domain β or reach out if you want help creating one.d307af6 -
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Verified signature# π Sentiment-RoBERTa-Base (by Raghavendra) A fine-tuned [RoBERTa-base](https://huggingface.co/roberta-base) model for **binary sentiment classification** (positive/negative). Trained on a custom dataset across multiple sources including tweets, social comments, and headlines to handle **real-world tone detection**. > β Use this model to build sentiment-aware applications, feedback classifiers, social media monitoring tools, or LLM content filters. --- ## π§ Model Details | Property | Value | |-----------------------|---------------------------| | Base Model | `roberta-base` | | Fine-tuned Tasks | Binary Sentiment Analysis | | Classes | `0 = Negative`, `1 = Positive` | | Language | English (`en`) | | Dataset | Custom multi-source | | Framework | π€ Transformers | --- ## π Evaluation (on 20% held-out test set) | Metric | Score | |-β¦9babcdd -
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