Update README.md

README.md

@@ -1,199 +1,84 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+datasets:
+- abisee/cnn_dailymail
+language:
+- ko
+- en
+metrics:
+- rouge
+base_model:
+- google/gemma-2-2b-it
+pipeline_tag: summarization
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for gemma-2-2b-it-research-in-a-flash
 
+- Fine-tune the Gemma2 2b model for summarizing scientific papers.
+- Filter the dataset for computer science papers to optimize training time.
+- Deploy the model on Hugging Face for easy accessibility.
 
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+his model is a fine-tuned version of `google/gemma-2-2b-it` on the `cnn_dailymail` dataset, designed for the task of **summarization**. 
+It can summarize paragraphs of text, especially from research papers or news articles, into concise summaries. 
+The model has been fine-tuned using the LoRA (Low-Rank Adaptation) method for parameter-efficient training.
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **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:** Changjip Moon
+- **Model type:** Summarization
+- **Language(s) (NLP):** Korean, English
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** google/gemma-2-2b-it
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Demo:** https://colab.research.google.com/drive/1xiyWCnTzXmFFgD7CBL-jq8m2Mv29fg-M?usp=sharing
 
 ## 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 model can be used to generate concise summaries of long texts. It is designed for summarizing academic papers, research materials, or news articles.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+### Downstream Use
+This model can be fine-tuned further for other languages or summarization-specific tasks like topic-based summarization.
 
 ### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is not designed for tasks outside of text summarization, such as text classification or question answering. It also may not perform well on non-news or non-research data.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+This model may have biases inherited from the `cnn_dailymail` dataset, which is mainly based on news articles in English. It may not perform well on non-news content or in cases where high precision is required for legal, medical, or sensitive content.
 
-[More Information Needed]
-
-### 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.
-
-## 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]
+The model was fine-tuned on the `cnn_dailymail` dataset, which contains articles and summaries from CNN and Daily Mail. The dataset is commonly used for text summarization tasks.
 
 ### Training Procedure
+The model was trained using the following hyperparameters:
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+- **Learning rate:** 2e-4
+- **Batch size:** 8 (with gradient accumulation steps of 4)
+- **Epochs:** 1
+- **Max sequence length:** 256
+- **Optimization method:** AdamW with 8-bit quantization
 
-#### Preprocessing [optional]
+#### Preprocessing
+Standard tokenization and truncation were applied. The maximum sequence length was set to 256 to balance memory usage and training speed.
 
-[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]
-
-#### Hardware
-
-[More Information Needed]
-
-#### 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]
-
-[More Information Needed]
+- **Training regime:** go to google colab pages if you want to know
 
-## Model Card Contact
+#### Speeds, Sizes, Times
 
-[More Information Needed]
+[2500/2500 22:33, Epoch 1/1] : Cause of timeout issue, I need to make a subset of data..