Update README.md

README.md

@@ -1,10 +1,10 @@
 ---
 library_name: transformers
-tags: []
+tags: [text-summarization, pegasus, fine-tuned, NLP]
 ---
 
 # Model Card for Model ID
-
+Model Card for Fine-Tuned Pegasus Summary Generator
 <!-- Provide a quick summary of what the model is/does. -->
 
 
@@ -14,22 +14,21 @@ tags: []
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
+This model is a fine-tuned version of the Pegasus model for text summarization, specifically optimized for generating structured summaries from transcripts. The model has been trained to capture key points, remove redundant information, and maintain coherence in summaries.
 
-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]
+- **Developed by:** Akshay Choudhary
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
+- **Model type:** Transformer-based summarization model
+- **Language(s) (NLP):** English
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model [optional]:** google/pegasus-large
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
+- **Repository:** https://huggingface.co/akshay9125/Transcript_Summerizer/
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
@@ -41,37 +40,68 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+The model can be directly used for transcript summarization in various applications, including:
+
+* Meeting and lecture transcript summarization
+
+* Podcast and interview summarization
+
+* Summarization of long-form text data
 
 ### 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]
+he model can be fine-tuned further for:
+
+* Domain-specific summarization (e.g., medical, legal, educational transcripts)
+
+* Integration into AI-powered note-taking tool
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+* Generating highly creative or fictional content
+
+* Summarizing extremely noisy or low-quality transcripts
+
+* Generating precise legal or medical documentation without expert verification
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
+The model may exhibit biases based on:
 
-[More Information Needed]
+T* he dataset used for fine-tuning
+
+* The quality and clarity of input transcripts
+
+* Potential loss of nuanced context in summarization
 
 ### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Users should:
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+* Validate summaries for critical use cases
 
+* Avoid using the model for tasks requiring absolute accuracy without human verification
+
+* Be aware of potential biases in summarization
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+
+tokenizer = PegasusTokenizer.from_pretrained("akshay9125/Transcript_Summerizer")
+model = PegasusForConditionalGeneration.from_pretrained("akshay9125/Transcript_Summerizer")
+
+def summarize_text(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="longest")
+    summary_ids = model.generate(**inputs)
+    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
 
 ## Training Details
 
@@ -79,11 +109,16 @@ Use the code below to get started with the model.
 
 <!-- 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]
+* Dataset: Collected and preprocessed transcript datasets
+
+* Preprocessing: Removal of noise, speaker labels, and unnecessary pauses
 
 ### 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: Tokenization with Pegasus tokenizer
+
+* Training regime: FP16 mixed precision
 
 #### Preprocessing [optional]
 
@@ -98,7 +133,7 @@ Use the code below to get started with the model.
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
+* Model size: ~568M parameters
 
 ## Evaluation
 
@@ -192,7 +227,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+**Akshay Choudhary**
 
 ## Model Card Contact