|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
language: |
|
- en |
|
base_model: |
|
- google/flan-t5-base |
|
--- |
|
|
|
# Model Card for FLAN-T5 QA Study Assistant |
|
|
|
This model is fine-tuned from the FLAN-T5 model to perform **extractive question-answering** tasks using the SQuAD dataset. It can generate answers based on the given context and question, which makes it useful for educational purposes, personal assistants, or any task requiring context-based question-answering. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
|
|
This is a **question-answering model** fine-tuned on a subset of the SQuAD dataset using the FLAN-T5 model. It is designed to extract answers from a given context based on a corresponding question. |
|
|
|
- **Developed by:** Tooba Javed |
|
- **Funded by [optional]:** Not funded |
|
- **Shared by [optional]:** Tooba Javed |
|
- **Model type:** Extractive Question-Answering Model (based on FLAN-T5) |
|
- **Language(s) (NLP):** English |
|
- **License:** Apache-2.0 License |
|
- **Finetuned from model [optional]:** google/flan-t5-base |
|
|
|
### Model Sources [optional] |
|
|
|
- **Repository:** [GitHub link to your notebook if applicable] |
|
- **Paper [optional]:** N/A |
|
- **Demo [optional]:** [Hugging Face link: https://huggingface.co/tootooba/flan-t5-qa-study-assistant] |
|
|
|
## Uses |
|
|
|
### Direct Use |
|
|
|
The model is intended to be used for answering questions based on provided context. It can be used in: |
|
- **Education**: To help students generate answers from textbooks or lecture notes. |
|
- **Customer Support**: Answer common questions from provided documentation or user manuals. |
|
- **Personal Assistants**: Assist users by answering general knowledge questions based on given text. |
|
|
|
### Downstream Use [optional] |
|
|
|
The model can be further fine-tuned for domain-specific question-answering tasks, such as: |
|
- Legal documents |
|
- Medical information |
|
- Company-specific internal knowledge bases |
|
|
|
### Out-of-Scope Use |
|
|
|
This model may not work well for tasks that require deep reasoning or understanding beyond surface-level extraction from text. Additionally, it is not designed for tasks like creative writing or opinion generation. |
|
|
|
## Bias, Risks, and Limitations |
|
|
|
- **Bias**: The model is trained on general knowledge from the SQuAD dataset, which may include biases present in Wikipedia-style articles. |
|
- **Risks**: Since the model relies on extracting information from provided context, it may return incomplete or misleading answers if the context is ambiguous or lacks the necessary information. |
|
- **Limitations**: The model cannot handle complex reasoning or multi-hop question-answering effectively. |
|
|
|
### Recommendations |
|
|
|
Users should ensure that: |
|
- They provide relevant and accurate context for better answers. |
|
- They are aware of potential biases from the dataset. |
|
- The model's limitations are accounted for in critical applications (e.g., legal or medical advice). |
|
|
|
## How to Get Started with the Model |
|
|
|
Use the code below to load the model and tokenizer for question-answering tasks. |
|
|
|
```python |
|
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
|
|
|
model = AutoModelForSeq2SeqLM.from_pretrained("tootooba/flan-t5-qa-study-assistant").to("cuda") |
|
tokenizer = AutoTokenizer.from_pretrained("tootooba/flan-t5-qa-study-assistant") |
|
|
|
context = "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It was constructed between 1887 and 1889 as the entrance arch for the 1889 World's Fair." |
|
question = "When was the Eiffel Tower constructed?" |
|
|
|
inputs = tokenizer(question, context, return_tensors="pt", truncation=True, padding=True).to("cuda") |
|
outputs = model.generate(inputs.input_ids) |
|
answer = tokenizer.decode(outputs[0], skip_special_tokens=True) |
|
|
|
print("Answer:", answer) |
|
|
