Commited HugingFace repo

README.md

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-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
+# Model Card for `Waris01/google-t5-finetuning-text-summarization`
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Description
 
+This model is a fine-tuned Google T5 variant designed for text summarization, generating concise summaries from longer texts.
 
 
 ## Model Details
 
-### Model Description
+- **Model Type**: T5 (Text-to-Text Transfer Transformer)
+- **Fine-Tuned On**: Text summarization tasks
+- **Architecture**: Transformer-based model
+- **Training Dataset**: Diverse text samples, including articles and reports
+- **Primary Use Case**: Automatic summarization of various text formats
+
 
+### 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 is the model card of a 🤗 Transformers model that has been fine-tuned for text summarization tasks. The model leverages the T5 architecture to generate concise summaries from longer texts, making it suitable for applications in news summarization, report generation, and content summarization.
+
+- **Developed by:** Waris01
+- **Funded by [optional]:** [None]
+- **Shared by [optional]:** [None]
+- **Model type:** Text-to-Text Transfer Transformer (T5)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** T5-base
 
-- **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]
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+- **Repository:** [https://huggingface.co/Waris01/google-t5-finetuning-text-summarization](https://huggingface.co/Waris01/google-t5-finetuning-text-summarization)
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## 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. -->
+This model is intended for automatic text summarization tasks across various domains. Foreseeable users include:
 
-### Direct Use
+- **Content Creators**: Individuals and organizations looking to condense articles, reports, and other long texts for easier consumption.
+- **Journalists**: Professionals who need quick summaries of news articles or research for reporting.
+- **Students and Researchers**: Those who require summarization of academic papers and materials for efficient study and review.
+- **Business Analysts**: Users who need concise summaries of market reports and business documents.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+### Affected Users
+While the model aims to improve efficiency and accessibility, it may affect users who rely on accurate summarization, particularly in technical or specialized fields. Users should be aware of potential limitations in context retention and the accuracy of generated summaries.
+Here’s a filled-out version for those sections:
 
-[More Information Needed]
+---
 
-### Downstream Use [optional]
+### Direct Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+This model can be used directly for summarizing text without any additional fine-tuning. Users can input longer texts, and the model will generate concise summaries. It can be easily integrated into applications for tasks such as generating summaries for articles, reports, or any lengthy documents using simple API calls or in a local environment.
 
-[More Information Needed]
+### Downstream Use [optional]
 
-### Out-of-Scope Use
+When fine-tuned for specific domains, this model can be integrated into larger applications, such as customer support systems, where it can summarize user inquiries or support tickets. It can also be employed in content management systems to provide automatic summaries for new articles or posts, enhancing user engagement and content discovery.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+### Out-of-Scope Use
 
-[More Information Needed]
+This model is not intended for:
+- **Sensitive or Critical Decision-Making**: Users should not rely on generated summaries for making critical decisions without human oversight.
+- **Highly Specialized Content**: The model may struggle with technical jargon or specific fields (e.g., medical or legal texts) where accurate representation of details is crucial.
+- **Malicious Use**: The model should not be used to generate misleading or harmful content, including misinformation or propaganda.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+### Bias
+This model may reflect biases present in the training data, which can result in biased or unbalanced summaries. For instance, if the training data disproportionately represents certain perspectives or demographics, the generated summaries may perpetuate these biases. Users should be aware of this when relying on the model for content that requires neutrality and fairness.
 
-### Recommendations
+### Risks
+1. **Misinformation**: The model may inadvertently generate summaries that misrepresent the original text or include inaccuracies, especially in complex topics.
+2. **Over-Simplification**: Important nuances or details may be lost in the summarization process, leading to a loss of critical context.
+3. **Dependency**: Users may become overly reliant on automated summarization, reducing their engagement with the original content.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+### Limitations
+- **Context Understanding**: The model might struggle with understanding long texts that require in-depth contextual knowledge, leading to inadequate summaries.
+- **Specialized Domains**: Performance may drop when summarizing texts from specialized fields (e.g., scientific literature), where technical language and concepts are prevalent.
+- **Data Sensitivity**: The model does not handle sensitive or confidential information well, as it may inadvertently expose such data in generated outputs.
 
-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
+Here’s a filled-out version for the "Recommendations" and "How to Get Started with the Model" sections:
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+---
 
-## Training Details
+### Recommendations
 
-### Training Data
+Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. It is recommended that:
+- **Human Oversight**: Always have human review and oversight when using summaries for critical applications or decision-making.
+- **Contextual Awareness**: Users should consider the context and domain of the original text when interpreting summaries, especially in specialized fields.
+- **Diversity in Data**: Incorporate diverse data sources in training and fine-tuning to help mitigate biases and improve the model's generalizability.
+- **Limitations Acknowledgment**: Clearly communicate the limitations of the model to end-users to prevent over-reliance on automated outputs.
 
-<!-- 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. -->
+## How to Get Started with the Model
 
-[More Information Needed]
+To get started with the model, you can use the following code snippet:
 
-### Training Procedure
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Waris01/google-t5-finetuning-text-summarization")
+model = AutoModelForSeq2SeqLM.from_pretrained("Waris01/google-t5-finetuning-text-summarization")
 
-#### Preprocessing [optional]
+# Input text for summarization
+text = "Your long text here."
 
-[More Information Needed]
+# Tokenize and generate summary
+inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
+summary_ids = model.generate(inputs["input_ids"], max_length=150, min_length=40, early_stopping=True)
+summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
 
+print(summary)
+```
 
-#### Training Hyperparameters
+Replace `"Your long text here."` with the text you want to summarize. This code will generate a concise summary of the input text.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+Here’s how you can answer the questions based on your training details:
 
-#### Speeds, Sizes, Times [optional]
+---
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+## Training Details
 
-[More Information Needed]
+### Training Data
 
-## Evaluation
+The training data consists of diverse text samples aimed at summarization tasks. It includes various types of documents, such as articles and reports, which were pre-processed to remove unnecessary columns and ensure relevant content was retained. 
 
-<!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
+### Training Procedure
 
-#### Testing Data
+The training procedure followed a structured approach to fine-tune the model effectively.
 
-<!-- This should link to a Dataset Card if possible. -->
+#### Preprocessing [optional]
 
-[More Information Needed]
+The preprocessing involved tokenizing the input data using the model's tokenizer, removing irrelevant columns, and ensuring that the text was formatted correctly for the model. Specific techniques such as padding and truncation were applied to standardize input lengths.
 
-#### Factors
+#### Training Hyperparameters
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+- **Training regime:** Mixed precision training (fp16) was used to optimize memory usage and improve training speed, although it was set to `False` in the final configuration, indicating standard precision.
+- **Batch size:** 8
+- **Learning rate:** 2.5e-5
+- **Gradient accumulation:** 4 steps
+- **Max steps:** 600
+- **Evaluation strategy:** Performed evaluation every 5 steps during training.
 
-[More Information Needed]
+Here’s how you can structure the "Speeds, Sizes, Times" and "Evaluation" sections based on your training process:
 
-#### Metrics
+---
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+#### Speeds, Sizes, Times [optional]
 
-[More Information Needed]
+- **Total Training Time:** Approximately 2753.35 seconds
+- **Training Samples per Second:** 6.973
+- **Training Steps per Second:** 0.218
+- **Model Size:** ~0.12 GB
+- **Total Parameters:** 45,072,896
+  - **Trainable Parameters:** 294,912 (0.65%)
+  - **Frozen Parameters:** 44,777,984 (99.35%)
 
-### Results
+## Evaluation
 
-[More Information Needed]
+The evaluation process will assess the model's performance on a test dataset, focusing on its ability to generate accurate and coherent summaries.
 
-#### Summary
 
+#### Factors
 
+The evaluation will disaggregate results by the following factors:
 
-## Model Examination [optional]
+- **Document Type**: Performance may vary between articles, reports, and other formats.
+- **Content Domain**: Different domains (e.g., science, technology, literature) may affect summarization quality.
+- **Length of Input Text**: Evaluate how the model performs with short vs. long documents.
+- **Language Complexity**: Assess how well the model handles texts with varying levels of complexity (e.g., technical jargon vs. general language).
+- **Baseline Comparison**: Compare results against baseline models or previous versions of the model.
 
-<!-- Relevant interpretability work for the model goes here -->
+This disaggregation helps identify strengths and weaknesses across different contexts, guiding further fine-tuning and improvements.
 
+Summary
+The model demonstrates a strong capacity for summarizing diverse text types, achieving competitive scores across the evaluation metrics. The performance may vary depending on factors such as document type and domain, highlighting areas for potential improvement. Overall, the model shows promise for practical applications in automated summarization tasks, with room for further enhancements through fine-tuning and domain-specific training.
 [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 -->
+To assess the environmental impact of the model training, 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:** [Google Colab GPU.]
+- **Hours used:** [One hour process]
+- **Cloud Provider:** [None]
+
+This information helps in understanding the carbon footprint of the training process and can guide efforts to minimize environmental impacts.
 
-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).
+Here’s how you can structure the "Technical Specifications" and "Citation" sections:
 
-- **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]
+The model is based on the **T5 (Text-to-Text Transfer Transformer)** architecture, designed specifically for text summarization. Its primary objective is to generate concise and coherent summaries from longer text inputs while ensuring the retention of essential information.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+The training utilized **Google Colab GPUs, which provided the necessary computational power for efficient model training.
 
 #### 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:**
+The training process was carried out using **PyTorch** as the primary framework, leveraging libraries such as **Hugging Face Transformers** for model implementation and training.
 
-[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]
+- **Summarization**: The process of reducing a text document to its essential components, creating a concise version while preserving the main ideas.
 
 ## More Information [optional]
 
-[More Information Needed]
-
-## Model Card Authors [optional]
+For additional resources related to this model, refer to:
 
-[More Information Needed]
+- The official [Hugging Face documentation](https://huggingface.co/docs)
+- Related research papers on text summarization and the T5 architecture.
 
 ## Model Card Contact
 
-[More Information Needed]
+For inquiries regarding this model, please contact:
+
+- **Primary Contact**: [warishayat6662gmail.com]