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README.md

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-library_name: transformers
-tags: []
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-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+---
+library_name: transformers
+pipeline_tag: text-classification
+---
 
+# Model Card for ModernBERT Fine-Tuned on Social Media Sentiment Analysis
 
+This model is a fine-tuned version of [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) tailored for sentiment analysis on social media data. ModernBERT is a modernized bidirectional encoder-only Transformer model pre-trained on 2 trillion tokens of English and code data, with a native context length of up to 8,192 tokens. :contentReference[oaicite:0]{index=0}
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This model is designed to perform sentiment analysis on social media text, classifying posts into positive, negative, or neutral sentiments. It leverages the advanced architecture of ModernBERT, which incorporates recent innovations in Transformer models to enhance performance and efficiency. :contentReference[oaicite:1]{index=1}
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** Chukwuebuka Ezeokeke
+- **Model type:** Encoder-only Transformer
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Fine-tuned from model:** [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [My Implementation](https://github.com/Chukwuebuka-2003/EbukaMBERT/blob/main/tunembert.ipynb)
+- **Paper:** [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://arxiv.org/abs/2412.13663)
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model can be directly used for sentiment analysis of English-language social media posts, aiding in understanding public opinion, monitoring brand sentiment, and analyzing user feedback.
 
-[More Information Needed]
+### Downstream Use
 
-### Downstream Use [optional]
+The model can be integrated into larger systems for tasks such as:
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+- **Customer Feedback Analysis:** Automating the analysis of customer sentiments from social media platforms.
+- **Market Research:** Gauging public reaction to products or events.
+- **Content Moderation:** Identifying potentially harmful or negative content.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model may not perform well on non-English text or on text that deviates significantly from social media language patterns. It is not designed for tasks outside sentiment analysis, such as topic modeling or language translation.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+While the model aims to provide accurate sentiment analysis, it may inherit biases present in the training data, especially those prevalent in social media language. Users should be cautious when deploying the model in sensitive applications and consider the potential for misclassification.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- **Bias Mitigation:** Regularly assess and mitigate biases by updating the training data and fine-tuning the model as needed.
+- **Performance Monitoring:** Continuously monitor the model's performance, especially when applied to new or evolving social media platforms.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
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-#### Hardware
-
-[More Information Needed]
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-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
+To use this model, you can load it with the Hugging Face `transformers` library:
 
-[More Information Needed]
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-## Model Card Contact
+model_name = "EbukaGaus/EbukaMBert"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-[More Information Needed]
+# Example usage
+inputs = tokenizer("I love using this new app!", return_tensors="pt")
+outputs = model(**inputs)