updated ReadME.md

README.md

@@ -5,140 +5,88 @@ task_categories:
 language:
 - en
 ---
-# Dataset Card for Dataset Name
+# Dataset Card for News Summarization
 
-<!-- Provide a quick summary of the dataset. -->
-
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+This dataset card documents the News Summary dataset used for training a T5 model specialized in summarization tasks, particularly focusing on news articles.
 
 ## Dataset Details
 
 ### Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
-
-
-
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-
-### Dataset Sources [optional]
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the dataset is intended to be used. -->
-
-### Direct Use
-
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
-
-## Dataset Structure
-
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-
-[More Information Needed]
+The News Summary dataset contains pairs of full-length news articles and their corresponding summaries. It was curated to train models that can generate concise and informative summaries of longer texts. This dataset is valuable for natural language processing tasks related to summarization.
 
-## Dataset Creation
+- **Curated by:** Sunny Srinidhi
+- **Shared by:** Kaggle
+- **Language(s) (NLP):** English
+- **License:** Dataset-specific license
 
-### Curation Rationale
+### Dataset Sources
 
-<!-- Motivation for the creation of this dataset. -->
+- **Repository:** [Kaggle Dataset Link](https://www.kaggle.com/datasets/sunnysai12345/news-summary)
 
-[More Information Needed]
+## Model Description
 
-### Source Data
+This T5 model is fine-tuned specifically for the task of summarizing news articles. It leverages the extensive pre-training of the T5 base model and adapts it to generate concise summaries of news content, aiming to maintain the core message and essential details.
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+## Training Procedure
 
-#### Data Collection and Processing
+The model was trained using the `Seq2SeqTrainer` from the Hugging Face Transformers library on a custom dataset. Training involved a sequence-to-sequence model that was fine-tuned with news article data, tokenized using the corresponding T5 tokenizer.
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+### Hyperparameters
 
-[More Information Needed]
+- **Evaluation Strategy:** Epoch
+- **Learning Rate:** 0.00002
+- **Train Batch Size per Device:** 8
+- **Eval Batch Size per Device:** 8
+- **Weight Decay:** 0.01
+- **Save Total Limit:** 2
+- **Number of Training Epochs:** 4
+- **Use FP16 Precision:** True
+- **Reporting:** None
 
-#### Who are the source data producers?
+## Training Metrics Table
 
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+| Epoch | Training Loss | Validation Loss | ROUGE-1                                  | ROUGE-2                                  | ROUGE-L                                  |
+|-------|---------------|-----------------|------------------------------------------|------------------------------------------|------------------------------------------|
+| 1     | No log        | 1.401181        | r: 17.15%, p: 63.80%, f: 26.83%          | r: 7.86%, p: 36.02%, f: 12.81%           | r: 15.90%, p: 59.24%, f: 24.89%          |
+| 2     | 1.594900      | 1.367020        | r: 17.47%, p: 65.14%, f: 27.36%          | r: 8.01%, p: 36.98%, f: 13.07%           | r: 16.17%, p: 60.43%, f: 25.33%          |
+| 3     | 1.461500      | 1.354850        | r: 17.68%, p: 65.80%, f: 27.67%          | r: 8.13%, p: 37.65%, f: 13.28%           | r: 16.34%, p: 60.95%, f: 25.58%          |
+| 4     | 1.434300      | 1.352294        | r: 17.77%, p: 66.08%, f: 27.81%          | r: 8.25%, p: 38.09%, f: 13.47%           | r: 16.45%, p: 61.30%, f: 25.75%          |
 
-[More Information Needed]
+Training Output: Global step=1692, training loss=1.4874611712516623, train runtime=1579.3283 seconds, samples per second=8.571, steps per second=1.071, total FLOPs=8232596872151040.0, epoch=4.
 
-### Annotations [optional]
-
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-
-#### Annotation process
-
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
-
-#### Personal and Sensitive Information
-
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+## Uses
 
-**BibTeX:**
+### Direct Use
 
-[More Information Needed]
+This dataset is primarily used for training and evaluating machine learning models on the summarization task. It is suitable for developing algorithms that require understanding and processing of news-style writing to produce summaries.
 
-**APA:**
+### Usage
 
-[More Information Needed]
+The model can be used directly via the Hugging Face `pipeline` for summarization tasks. Here is a sample code snippet:
 
-## Glossary [optional]
+```python
+from transformers import pipeline
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
+# Load the model
+model = AutoModelForSeq2SeqLM.from_pretrained("t5-news")
+tokenizer = AutoTokenizer.from_pretrained("t5-news")
 
-[More Information Needed]
+# Create summarizer pipeline
+summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
 
-## More Information [optional]
+# Summarize text
+text = "Your news article text here"
+print(summarizer(text))
+```
 
-[More Information Needed]
+### Out-of-Scope Use
 
-## Dataset Card Authors [optional]
+The dataset may not be suitable for tasks requiring fine-grained sentiment analysis, detailed factual extraction, or tasks outside the English language.
 
-[More Information Needed]
+## Evaluation Metrics
+The model was evaluated using ROUGE metrics which measure the overlap of n-grams between the generated summaries and reference summaries. This metric is standard for evaluating summarization models.
 
-## Dataset Card Contact
+## Conclusion
+This T5 model provides a robust solution for summarizing news articles, equipped to handle a variety of news formats and contents effectively. It is particularly useful for applications requiring quick generation of concise summaries from lengthy news articles.
 
-[More Information Needed]