Model Name: fint5-financeqa
Base Model: t5-small
Domain: Financial Question Answering
Fine-tuned by: [Your Name]
Training Environment: Google Colab
License:MIT (inherits from t5-small)
📚 Description
fint5-financeqa is a fine-tuned version of the t5-small model, tailored for financial question-answering tasks using the sweatSmile/FinanceQA dataset.
The model is capable of answering domain-specific questions when given both the question and relevant context. This makes it suitable for chatbot assistants, enterprise finance automation, or as a component in investment-related QA systems.
📦 Intended Use
You can use this model to:
Build finance-domain QA bots
Help users query financial reports
Perform automated FAQ answering for fintech tools
Not recommended for:
General-purpose QA tasks (use base T5 instead)
Time-sensitive financial forecasting or advice
Legal or regulatory decision-making
🧠 Training Details
Detail
Value
Base Model
t5-small
Dataset
sweatSmile/FinanceQA
Dataset Size
3705 total entries (80/20 split)
Preprocessing
QUERY + context: CONTEXT
Target Text
ANSWER string
Max Input Length
512 tokens
Max Target Length
128 tokens
Epochs
3
Batch Size
4
Optimizer & LR
AdamW @ 2e-5
Mixed Precision
Enabled (fp16)
Evaluation Strategy
Per Epoch
Frameworks
🤗 Transformers, Datasets, Accelerate
📈 Metrics
Metric
Value
Final Eval Loss
0.415
BLEU / ROUGE
[Not explicitly printed]
EM / Accuracy
[Can be added]
Note: Evaluation beyond loss was mentioned but not shown. Future versions can include BLEU, ROUGE, or exact match scores using evaluate or sacrebleu.
📉 Training Curves
Loss chart from the training loop:
Training Curve Placeholder
Replace with actual matplotlib plot or embed image if hosted.
🧪 Example Usage
python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained("./fint5-financeqa")
tokenizer = T5Tokenizer.from_pretrained("./fint5-financeqa")
question = "What is the net income of the company?"
context = "The company reported a net income of $1.5 million for the last quarter."
