| | --- |
| | license: mit |
| | base_model: facebook/bart-large-cnn |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - rouge |
| | model-index: |
| | - name: conversation-summ |
| | results: [] |
| | datasets: |
| | - har1/MTS_Dialogue-Clinical_Note |
| | language: |
| | - en |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # HealthScribe (A Clinical Note Generator) |
| |
|
| | This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on a modified version of [MTS-Dialog Dataset](https://github.com/abachaa/MTS-Dialog) dataset. |
| |
|
| |
|
| | ## Model description |
| |
|
| | The model was developed for the project [HealthScirbe](https://github.com/hari-krishnan-88/HealthScribe-Clinical_Note_Generator). This model is integrated with a Flask web application. The project is a web application that allows users to generate clinical notes from transcribed ASR(Automatic Speech Recognition) data of conversations between doctors and patients. |
| |
|
| | ### TEST DATA Sample For Inference (More given in [`test.txt`](https://huggingface.co/har1/HealthScribe-Clinical_Note_Generator/blob/main/test.txt)) |
| |
|
| | You can refer [`test.txt`](https://huggingface.co/har1/HealthScribe-Clinical_Note_Generator/blob/main/test.txt) for further examples of conversations. |
| |
|
| | ``` |
| | "Doctor: Hi there, I love that dress, very pretty! |
| | Patient: Thank you for complementing a seventy-two-year-old patient. |
| | Doctor: No, I mean it, seriously. Okay, so you were admitted here in May two thousand nine. You have a history of hypertension, and on June eighteenth two thousand nine you had bad abdominal pain diarrhea and cramps. |
| | Patient: Yes, they told me I might have C Diff? They did a CT of my abdomen and that is when they thought I got the infection. |
| | Doctor: Yes, it showed evidence of diffuse colitis, so I believe they gave you IV antibiotics? |
| | Patient: Yes they did. |
| | Doctor: Yeah I see here, Flagyl and Levaquin. They started IV Reglan as well for your vomiting. |
| | Patient: Yes, I was very nauseous. Vomited as well. |
| | Doctor: After all this I still see your white blood cells high. Are you still nauseous? |
| | Patient: No, I do not have any nausea or vomiting, but still have diarrhea. Due to all that diarrhea I feel very weak. |
| | Doctor: Okay. Anything else any other symptoms? |
| | Patient: Actually no. Everything's well. |
| | Doctor: Great. |
| | Patient: Yeah." |
| | ``` |
| |
|
| |
|
| | ## Intended uses & limitations |
| |
|
| | The model is used to generate clinical notes from doctor-patient conversation data(ASR). This model has certain limitations like : |
| | - N/A output generation is low. Sometimes None is produced |
| | - When the input data is composed of very minimal character tokens or if input is very large it starts to hallucinate. |
| |
|
| |
|
| | # Training Metrics |
| |
|
| | ## Training and evaluation data |
| |
|
| | The model achieves the following results on the evaluation set: |
| |
|
| | - **Loss:** 0.1562 |
| | - **Rouge1:** 54.3238 |
| | - **Rouge2:** 34.2678 |
| | - **Rougel:** 46.5847 |
| | - **Rougelsum:** 51.2214 |
| | - **Generation Length:** 77.04 |
| |
|
| |
|
| | ## Training procedure |
| |
|
| | The model was trained on 1201 training samples and 100 validation samples of the modified [MTS-Dialog](https://huggingface.co/datasets/har1/MTS_Dialogue-Clinical_Note) |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - ```learning_rate```: 2e-05 |
| | - ```train_batch_size```: 1 |
| | - ```eval_batch_size```: 1 |
| | - ```seed```: 42 |
| | - ```gradient_accumulation_steps```: 2 |
| | - ```total_train_batch_size```: 2 |
| | - ```optimizer```: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - ```lr_scheduler_type```: linear |
| | - ```num_epochs```: 3 |
| | - ```mixed_precision_training```: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
| | | 0.4426 | 1.0 | 600 | 0.1588 | 52.8864 | 33.253 | 44.9089 | 50.5072 | 69.38 | |
| | | 0.1137 | 2.0 | 1201 | 0.1517 | 56.8499 | 35.309 | 48.2171 | 53.6983 | 72.74 | |
| | | 0.0796 | 3.0 | 1800 | 0.1562 | 54.3238 | 34.2678 | 46.5847 | 51.2214 | 77.04 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.39.2 |
| | - Pytorch 2.2.1+cu121 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.15.2 |
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