omark807
/

dsb-biases-model

@@ -1,202 +1,87 @@
 ---
 base_model: google/flan-t5-large
 library_name: peft
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.15.2

 ---
 base_model: google/flan-t5-large
 library_name: peft
+tags:
+- text-generation
+- question-answering
+- bias-mitigation
+- flan-t5
+- lora
+- peft
+- disability-rights
+- accessibility
+- social-impact
 ---
+# Flan-T5-Large LoRA Adapter for Disability Q&A and Mitigating Disability Biases
+## Model Description
+This repository contains a LoRA (Low-Rank Adaptation) adapter fine-tuned on the `google/flan-t5-large` base model. The adapter is specifically trained for **improving question-answering capabilities related to disability information and actively reducing harmful biases and stereotypes concerning people with disabilities in generated text.**
+This model leverages the PEFT (Parameter-Efficient Fine-Tuning) library to efficiently adapt the large Flan-T5 model to this specialized domain without requiring full model retraining, making it more resource-efficient and deployable.
+-   **Developed by:** omark807
+-   **Finetuned from model:** `google/flan-t5-large`
+-   **Model type:** Adapter (LoRA) for Sequence-to-Sequence Language Model
+-   **Language(s) (NLP):** English
+-   **License:** GPL
+### Base Model Details (`google/flan-t5-large`)
+Flan-T5 is an instruction-tuned variant of the T5 text-to-text transformer model. It has been fine-tuned on a collection of datasets expressed as natural language instructions. The "large" version has approximately 770 million parameters. This adapter builds upon its strong instruction-following capabilities.
+* **Original Model Card:** [https://huggingface.co/google/flan-t5-large](https://huggingface.co/google/flan-t5-large)
 ## Uses
 ### Direct Use
+This adapter is intended to be loaded alongside the `google/flan-t5-large` model using the PEFT library. It can then be used for:
+* **Answering questions** related to various aspects of disability, accessibility, disability rights, legislation, and common challenges.
+* **Generating responses** that are more inclusive, respectful, and free from common disability biases and stereotypes.
+* **Providing information** in a neutral and empathetic tone when discussing disability-related topics.
+**Example Inference for Q&A:**
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+import torch
+# Load the base model
+model_name = "google/flan-t5-large"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+# Load your adapter
+# Replace "your-huggingface-username/your-repo-name" with your actual model ID
+adapter_model_id = "[your-huggingface-username]/[your-repo-name]"
+model = PeftModel.from_pretrained(model, adapter_model_id)
+model.eval() # Set model to evaluation mode
+# Example inference for Q&A
+# Input: "What is the Americans with Disabilities Act (ADA)?"
+# Expected Output: A concise explanation of the ADA.
+input_text_qa = "question: What is the Americans with Disabilities Act (ADA)?"
+input_ids_qa = tokenizer(input_text_qa, return_tensors="pt").input_ids
+with torch.no_grad():
+    outputs_qa = model.generate(input_ids_qa, max_new_tokens=100, num_beams=5, early_stopping=True)
+decoded_output_qa = tokenizer.decode(outputs_qa[0], skip_special_tokens=True)
+print(f"Input (Q&A): {input_text_qa}")
+print(f"Output (Q&A): {decoded_output_qa}")
+# Example inference for Bias Mitigation/Instruction Following
+# Input: "Rewrite the following sentence to remove any ableist language: 'He was confined to a wheelchair.'"
+# Expected Output: "He used a wheelchair." or similar respectful phrasing.
+input_text_bias = "instruction: Rewrite the following sentence to remove any ableist language: 'He was confined to a wheelchair.'"
+input_ids_bias = tokenizer(input_text_bias, return_tensors="pt").input_ids
+with torch.no_grad():
+    outputs_bias = model.generate(input_ids_bias, max_new_tokens=50, num_beams=5, early_stopping=True)
+decoded_output_bias = tokenizer.decode(outputs_bias[0], skip_special_tokens=True)
+print(f"Input (Bias Mitigation): {input_text_bias}")
+print(f"Output (Bias Mitigation): {decoded_output_bias}")