Model Card for Hridayam
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: [Rohan Pashikanti, Sushma Uppu, Srija Patha, Sreemai Annam]
- Model type: [Text2Text Generation]
- Language(s) (NLP): [English]
- Finetuned from model: [facebook/blenderbot-400M-distill]
Model Sources
- Repository: Hugging Face Hub Repository
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
This model is designed to perform text-to-text generation tasks, useful in scenarios like chatbot responses, summarization, or other NLP tasks requiring a transformation of text input to text output.
Downstream Use [optional]
The model could be further fine-tuned on specific datasets to tailor it to specialized medical, legal, or technical domains, enhancing its utility in domain-specific applications.
Out-of-Scope Use
The model is not suitable for:
- Generating legally binding advice without supervision.
- High-stakes decision-making without human oversight.
Bias, Risks, and Limitations
The model may inherit biases from the training data or exhibit unpredictable behavior when presented with text outside the scope of its training data.
Recommendations
Users should validate the model's output for accuracy and bias, especially when used in sensitive contexts. Regular updates and monitoring are recommended to mitigate potential risks.
How to Get Started with the Model
1from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
2
3tokenizer = AutoTokenizer.from_pretrained("Hridayam")
4model = AutoModelForSeq2SeqLM.from_pretrained("Hridayam")
5
6input_text = "Example input text"
7inputs = tokenizer(input_text, return_tensors="pt")
8outputs = model.generate(inputs["input_ids"])
9print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Details
Training Data
The model was fine-tuned on a diverse dataset curated to include a wide range of medical texts, ensuring broad coverage of medical terminology and concepts.
Training Procedure
Preprocessing
Data was preprocessed to remove any personal identifiable information and to normalize medical terminology.
Training Hyperparameters
- Optimizer: AdamW
- Learning Rate: 2e-5
- Batch Size: 16
- Epochs: 4
Speeds, Sizes, Times
- Training time: Approximately 72 hours on V100 GPUs.
Evaluation
Testing Data, Factors & Metrics
Testing Data
Evaluated on a held-out dataset that was not seen during training to ensure the model generalizes well to new, unseen data.
Factors
Performance was disaggregated by various medical subfields to ensure broad competency across different types of medical texts.
Metrics
Results
The model achieved an accuracy of 85% and an F1 score of 0.82 on the testing dataset.
Environmental Impact
- Hardware Type: NVIDIA Tesla V100
- Hours used: 72
- Cloud Provider: AWS
- Compute Region: US East
- Carbon Emitted: Estimated 75 kg CO2eq
Technical Specifications
Model Architecture and Objective
The model is based on the BlenderBot architecture, adapted for sequence-to-sequence tasks, focusing on generating coherent and contextually appropriate text responses.
Compute Infrastructure
Hardware
Used NVIDIA Tesla V100 GPUs, optimized for deep learning tasks.
Software
Trained using the PyTorch framework with Hugging Face's Transformers library.
Citation [optional]
BibTeX:
1@article{HridayamModel,
2 title={Hridayam: A Fine-Tuned BlenderBot Model for Medical Text Generation},
3 author={Pashikanti, Rohan and Uppu, Sushma and Patha, Srija and Annam, Sreemai},
4 year={2025},
5 journal={Hugging Face Model Hub}
6}
APA:
Pashikanti, R., Uppu, S., Patha, S., & Annam, S. (2025). Hridayam: A Fine-Tuned BlenderBot Model for Medical Text Generation. Hugging Face Model Hub.
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