Update README.md

README.md

@@ -17,187 +17,113 @@ base_model: facebook/bart-large
 
 
 
-- **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:** Nevidu Jayatilleke and Ruvan Weerasinghe
+<!-- - **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed] -->
+<!-- - **Model type:** [More Information Needed] -->
+- **Supported Language:** English
+<!-- - **License:** [More Information Needed] -->
+- **Finetuned from model:** facebook/bart-large
 
-### Model Sources [optional]
+### Model Sources
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+<!-- - **Repository:** [More Information Needed] -->
+- **Paper:** The model was published in "A Hybrid Architecture with Efficient Fine Tuning
+for Abstractive Patent Document Summarization" available in https://arxiv.org/abs/2503.10354
 
-## 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 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]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[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 (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
+## How to use 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]
-
-### 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]
-
-
-#### 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]
-
-## Model Card Contact
-
-[More Information Needed]
-
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import nltk
+from nltk.tokenize import sent_tokenize, word_tokenize
+from nltk.corpus import stopwords
+from nltk.cluster.util import cosine_distance
+import numpy as np
+import networkx as nx
+import pandas as pd
+
+def preprocess_text(text):
+    sentences = sent_tokenize(text)
+    tokenized_sentences = [word_tokenize(sentence.lower()) for sentence in sentences]
+    return tokenized_sentences
+
+def sentence_similarity(sentence1, sentence2):
+    stop_words = set(stopwords.words('english'))
+    filtered_sentence1 = [w for w in sentence1 if w not in stop_words]
+    filtered_sentence2 = [w for w in sentence2 if w not in stop_words]
+    all_words = list(set(filtered_sentence1 + filtered_sentence2))
+    vector1 = [filtered_sentence1.count(word) for word in all_words]
+    vector2 = [filtered_sentence2.count(word) for word in all_words]
+    return 1 - cosine_distance(vector1, vector2)
+
+def build_similarity_matrix(sentences):
+    similarity_matrix = np.zeros((len(sentences), len(sentences)))
+    for i in range(len(sentences)):
+        for j in range(len(sentences)):
+            if i != j:
+                similarity_matrix[i][j] = sentence_similarity(sentences[i], sentences[j])
+    return similarity_matrix
+
+def apply_lexrank(similarity_matrix, damping=0.85, threshold=0.2, max_iter=100):
+    nx_graph = nx.from_numpy_array(similarity_matrix)
+    scores = nx.pagerank(nx_graph, alpha=damping, tol=threshold, max_iter=max_iter)
+    return scores
+
+def get_top_sentences(sentences, scores):
+    ranked_sentences = sorted(((scores[i], sentence) for i, sentence in enumerate(sentences)), reverse=True)
+    top_sentences = [sentence for score, sentence in ranked_sentences]
+    return top_sentences
+
+def extract_important_sentences(text):
+    preprocessed_sentences = preprocess_text(text)
+    similarity_matrix = build_similarity_matrix(preprocessed_sentences)
+    scores = apply_lexrank(similarity_matrix)
+    top_sentences = get_top_sentences(preprocessed_sentences, scores)
+    paragraph = ' '.join([' '.join(sentence) for sentence in top_sentences])
+    return paragraph
+
+def summarize(text, max_tokens):
+
+    peft_model = "Nevidu/LexBartLo_1"
+    config = PeftConfig.from_pretrained(peft_model)
+
+    # load base LLM model and tokenizer
+    model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)
+    tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+    # Load the Lora model
+    model = PeftModel.from_pretrained(model, peft_model)
+
+    sorted_text = extract_important_sentences(text)
+
+    input_ids = tokenizer(sorted_text, return_tensors="pt", truncation=True).input_ids
+    # with torch.inference_mode():
+    outputs = model.generate(input_ids=input_ids, max_new_tokens=max_tokens, do_sample=True, top_p=0.9)
+    summary = tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]
+    return summary
+
+text = """ Add your textile patent text"""
+max_tokens = 256
+
+summary = summarize(text, max_tokens)
+```
+
+## Citation
+
+```json
+@article{jayatilleke2025hybrid,
+  title={A Hybrid Architecture with Efficient Fine Tuning for Abstractive Patent Document Summarization},
+  author={Jayatilleke, Nevidu and Weerasinghe, Ruvan},
+  journal={arXiv preprint arXiv:2503.10354},
+  year={2025}
+}
+```
 
 ### Framework versions