Upload README.md with huggingface_hub

README.md

@@ -1,199 +1,113 @@
 ---
-library_name: transformers
-tags: []
+language:
+  - en
+license: apache-2.0
+base_model: emilyalsentzer/Bio_ClinicalBERT
+tags:
+  - medical
+  - clinical
+  - ssi
+  - classification
+  - surveillance
+  - multi-label
+metrics:
+  - accuracy
+  - f1
+  - precision
+  - recall
+model-index:
+  - name: SSIBERT-multiclass
+    results:
+      - task:
+          type: text-classification
+          name: Multi-Label SSI Detection
+        dataset:
+          name: Synthetic UK NHS Clinical Notes (Multi-Label)
+          type: synthetic
+          split: test
+        metrics:
+          - name: F1 (Micro)
+            type: f1
+            value: 1.0
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for Ch3DS/SSIBERT-multiclass
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-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:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
+This model is a fine-tuned version of [Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) designed for **multi-label classification** of postoperative clinical notes. Unlike the binary SSI model, this model identifies specific clinical indicators of infection:
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+1.  **Purulence**: Presence of pus or purulent discharge.
+2.  **Redness**: Erythema, spreading redness, or inflammation.
+3.  **Fever**: Pyrexia, rigors, or elevated temperature.
+4.  **Antibiotics**: Prescription of antibiotics (treatment or prophylaxis).
+5.  **SSI**: Overall determination of Surgical Site Infection.
 
-### Direct Use
+It is tailored to **UK NHS terminology**.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+- **Developed by:** Daryn Sutton
+- **Model type:** Multi-Label Text Classification (BERT)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT)
+- **Repository:** [https://huggingface.co/Ch3DS/SSIBERT-multiclass](https://huggingface.co/Ch3DS/SSIBERT-multiclass)
 
-[More Information Needed]
+### Uses
 
-### Downstream Use [optional]
+#### Direct Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+This model extracts structured data from unstructured clinical notes, allowing for more granular surveillance.
 
-[More Information Needed]
+- **Input**: Clinical note text.
+- **Output**: Probabilities for `[Purulence, Redness, Fever, Antibiotics, SSI]`.
 
-### Out-of-Scope Use
+#### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- **Diagnosis**: This is a surveillance tool, not a diagnostic device.
+- **Non-UK Contexts**: May perform poorly on non-NHS terminology.
 
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
+## How to Get Started with the Model
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+model_name = "Ch3DS/SSIBERT-multiclass"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-## How to Get Started with the Model
+text = "Day 3 post THR. Wound oozing pus. Patient pyrexial. Plan: Start Flucloxacillin."
+inputs = tokenizer(text, return_tensors="pt")
 
-Use the code below to get started with the model.
+with torch.no_grad():
+    logits = model(**inputs).logits
+    probs = torch.sigmoid(logits)
 
-[More Information Needed]
+labels = ["Purulence", "Redness", "Fever", "Antibiotics", "SSI"]
+for i, label in enumerate(labels):
+    print(f"{label}: {probs[0][i]:.2%}")
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+- **Source**: 5 million synthetic clinical notes.
+- **Methodology**: Generated using templates based on UK NHS terminology and the PRAISE network's surveillance definitions.
+- **Labels**: Multi-hot encoded.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Epochs**: 3
+- **Batch Size**: 64
+- **Hardware**: NVIDIA GeForce RTX 5070 Ti
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+Evaluated on a held-out test set of synthetic data. Achieved near-perfect performance on the synthetic distribution.
 
 ## Model Card Contact
 
-[More Information Needed]
+**Daryn Sutton**