Update README.md

README.md

@@ -1,6 +1,12 @@
 ---
 library_name: transformers
-tags: []
+language:
+- en
+metrics:
+- rouge
+- meteor
+base_model:
+- facebook/bart-large
 ---
 
 # Model Card for Model ID
@@ -17,160 +23,95 @@ tags: []
 
 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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
+- **Developed by:** Gowni Bhavishya,Dr.Shib Shankar sahu
+- **Model type:** Sequence-to-Sequence model (BART) fine-tuned for scientific highlight generation
+- **Language(s) (NLP):** English
+- **Finetuned from model [optional]:** facebook/bart-large.
 
 - **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. -->
+1. Researchers in biomedical and scientific fields
+2. Academic publishers and editors
+3. Developers building scientific summarization tools
+4. NLP practitioners working on domain-specific summarization
 
 ### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Generate highlights or concise summaries of scientific abstracts (especially biomedical, life sciences, or clinical research)
 
 [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]
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+1. Not suitable for general news summarization, social media content, or informal language.
+2. Should not be used for critical medical decision-making or clinical diagnostics.
+3. Not designed for creative writing, dialogue generation, or question answering.
+4. Avoid using this model for non-English abstracts or multilingual input—it was trained on English biomedical text only.
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+While BART performs well on biomedical abstracts, it inherits limitations from both:
+1. Pretrained BART model biases (from general corpora like Wikipedia and Books)
+2. Training dataset distribution biases (e.g., if your abstracts are from PubMed or a niche field)
+Known Limitations:
+1. May generate generic summaries if abstracts are vague or long.
+2. Struggles with mathematical, chemical, or symbolic notation.
+3. Output may appear plausible but factually incorrect.
+4. Does not provide citations or references for claims.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
+1. Always validate generated summaries against the full abstract or ground truth highlights.
+2. Preferably use in human-in-the-loop systems where an expert reviews the output.
+3. Fine-tune further or filter input for domain-specific tasks (e.g., cardiology vs oncology).
 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]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
+1.Fine-tuned on a dataset of scientific abstracts and their corresponding highlights.
+The training dataset was split into train (10k), validation (2k), and test (1.8k) sets.
+Input: Abstract column
+Target: Highlights column (only in train/val)
 
 #### 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]
+Model architecture: facebook/bart-large
+Batch size: 4 (per device)
+Epochs: 5
+Learning rate: 2e-5
 
-<!-- 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. -->
+Rouge1,Rouge2,RougeL,Meteor.
 
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
+The test set consists of 1,840 scientific abstracts without ground-truth highlights.
 
-[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]
+ROUGE-1: Measures unigram overlap (precision & recall)
+ROUGE-2: Measures bigram overlap
+ROUGE-L: Measures longest common subsequence
+METEOR: Incorporates synonymy, stemming, and word order
 
 ### 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:**
 
@@ -180,15 +121,10 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 [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]
+SVNIT CSE 
 
 ## Model Card Authors [optional]