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--- |
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library_name: transformers |
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tags: |
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- text summarization |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- rouge |
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pipeline_tag: text2text-generation |
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--- |
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# Model Card for Post-Disaster Digital Help Desk Summarization Model |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model is designed to summarize digital help desk conversations in post-disaster scenarios, specifically tailored for non-profit organizations providing aid. It is based on the BART model, fine-tuned using parameter-efficient methods like LoRa adapters. |
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## Model Details |
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### Model Description |
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This is a parameter efficient finetuned model based on the fine-tuning of the BART model. the methodology used is the LoRa adapter. this model focuses on automated text summarization of digital helpdesk conversations in post-disaster assistance scenarios in order to improve the efficiency and quality of the information gathered to provide timely and effective support to the affected people. |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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The model is designed to summarize digital help desk conversations for nonprofit organizations in post-disaster assistance scenarios, helping digital help desk staff to quickly extract key information and reduce the time it takes to manually write high-quality summaries. |
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## Bias, Risks, and Limitations |
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Generated summaries may contain certain errors, such as the inclusion of sensitive information, and require manual secondary correction to ensure accuracy and privacy protection. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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# install package |
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!pip install transformers[torch] -U |
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!pip install -q -U peft |
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import os |
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import torch |
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from google.colab import drive |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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from huggingface_hub import notebook_login |
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# login to hugging_face |
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notebook_login() # use model on GPU |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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# load base model |
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model_name = "knkarthick/MEETING_SUMMARY" |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# load trained adapter |
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adapter_id = "Joaaaane/510_ABW_LoRaAdapter_PostDisasterConv" |
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model.load_adapter(adapter_id) # set the model to evaluation mode |
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model.eval() |
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input_text = """ |
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PA: Hello, I need urgent housing help as a refugee from Ukraine. Can you assist? |
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agent: Hello, thank you for reaching out to the Red Cross. We’re here to help with housing. |
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agent: Have you registered with the local authorities yet? |
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PA: Yes, but they mentioned delays, and we need something soon. It's urgent. |
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agent: We have temporary shelters available. How many are with you, and are there any special needs? |
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PA: It's just me and my elderly mother; we need accessible housing. |
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agent: We can arrange for accessible temporary shelter. I’ll expedite your request and aim to place you within a few days. |
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agent: I'll also connect you with a Ukrainian-speaking volunteer to help with your paperwork and make your mother more comfortable. |
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PA: Thank you so much. This help means a lot to us right now. |
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agent: You're welcome! Expect a call from our volunteer by tomorrow. We’ll make sure you both are settled quickly. |
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PA: Thanks again. Looking forward to resolving this soon. |
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""" |
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# tokenized inputs |
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inputs = tokenizer(input_text, return_tensors="pt", max_length=1024, truncation=True).to(device) |
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# generate summary tokens |
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outputs = model.generate(inputs['input_ids'], max_length=62, num_beams=5, early_stopping=True) |
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# decode tokens |
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summary = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print("Generated Summary:", summary) |
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## Training Details |
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### Training Data |
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Data provided by 510, an initiative of the Netherlands Red Cross (all confidential data has been masked). |
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### Testing Data |
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Data provided by 510, an initiative of the Netherlands Red Cross (all confidential data has been masked). |
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### Metrics |
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ROUGE Score |
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### Results |
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| Metric | Before LoRA | After LoRA | |
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|--------------------|-------------|------------| |
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| **ROUGE 1** | 22.50 | 28.30 | |
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| **ROUGE 2** | 4.96 | 8.64 | |
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| **ROUGE L** | 17.24 | 22.50 | |
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## Citation |
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Base model: https://huggingface.co/knkarthick/MEETING_SUMMARY |
