Model Card for Model ID
Model Details
Model Description
RoBERTa-base fine-tuned using the LoRA (Low-Rank Adaptation) method
for binary sentiment classification (POSITIVE / NEGATIVE).
- Developed by: [Diary/Diary14]
- Funded by [optional]: [More Information Needed]
- Shared by [Diary]: [(https://huggingface.co/FacebookAI/roberta-base)]
- Model type: [Encoder-only Transformer (RoBERTa) — Sequence Classification]
- Language(s) (NLP): [English]
- License: [MIT]
- Finetuned from model [FacebookAI/roberta-base]: [More Information Needed]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
Classify English texts as POSITIVE or NEGATIVE without
additional fine-tuning. Direct use cases:
- Analysis of product reviews (e-commerce, Amazon, etc.)
[More Information Needed]
Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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- Hours used: [More Information Needed]
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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Glossary [optional]
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