Update README.md

README.md

@@ -3,197 +3,96 @@ library_name: transformers
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-# Model Card for Model ID
+# 🧠 GLiClass Gender Classifier — DeBERTaV3 Uni-Encoder (3-Class)
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model is designed for **text classification** in clinical narratives, specifically for determining a patient's **sex or gender**. It was fine-tuned using a **uni-encoder architecture** based on [`microsoft/deberta-v3-small`](https://huggingface.co/microsoft/deberta-v3-small), and outputs one of three labels:
 
+- `male`
+- `female`
+- `sex undetermined`
 
+---
 
-## 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
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## 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]
-
-### 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]
-
-## 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]
+## 🧪 Task
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+This is a **multi-class text classification** task over **clinical free-text**. The model predicts the gender of a patient from discharge summaries, case descriptions, or medical notes.
 
-[More Information Needed]
 
-## More Information [optional]
+> ⚠️ **It is strongly recommended to keep the labels and the input text in the same language** (e.g., both in Spanish or both in English) to ensure optimal model performance. Mixing languages may reduce accuracy.
+---
 
-[More Information Needed]
+## 🧩 Model Architecture
 
-## Model Card Authors [optional]
+- Base: `microsoft/deberta-v3-small`
+- Architecture: `DebertaV2ForSequenceClassification`
+- Fine-tuned with a **uni-encoder** setup
+- 3 output labels
 
-[More Information Needed]
+---
 
-## Model Card Contact
+## 🔍 Input Format
+
+Each input sample must be a JSON object like this:
+
+```json
+{
+  "text": "Paciente de 63 años que refería déficit de agudeza visual (AV)...",
+  "all_labels": ["male", "female", "sex undetermined"],
+  "true_labels": ["sex undetermined"]
+}
+
+## Usage example
+import json
+from transformers import AutoTokenizer
+from gliclass import GLiClassModel, ZeroShotClassificationPipeline
+import torch
+
+device = 0 if torch.cuda.is_available() else -1
+model_path = "BSC-NLP4BIA/GLiClass-gender-classifier"
+classification_type = "single-label"  # or "multilabel"
+test_path = "path/to/your/test_data.json"
+
+print(f"🔄 Loading model from {model_path}...")
+model = GLiClassModel.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model.to(device)
+
+pipeline = ZeroShotClassificationPipeline(
+    model=model,
+    tokenizer=tokenizer,
+    classification_type=classification_type,
+    device=device
+)
+
+with open(test_path, 'r') as f:
+    test_data = json.load(f)
+
+# 🔍 Automatically infer candidate labels from the dataset
+all_labels = set()
+for sample in test_data:
+    all_labels.update(sample["true_labels"])
+candidate_labels = sorted(all_labels)
+
+print(f"🧾 Candidate labels inferred: {candidate_labels}")
+
+results = []
+
+for sample in test_data:
+    true_labels = sample["true_labels"]
+    output = pipeline(sample["text"], candidate_labels)
+    top_results = output[0]
+
+    predicted_labels = [max(top_results, key=lambda x: x["score"])["label"]]
+    score_dict = {d["label"]: d["score"] for d in top_results}
+    
+    entry = {
+        "text": sample["text"],
+        "true_labels": true_labels,
+        "predicted_labels": predicted_labels
+    }
+    # Add scores for each candidate label
+    for label in candidate_labels:
+        entry[f"score_{label}"] = score_dict.get(label, 0.0)
+
+    results.append(entry)
 
-[More Information Needed]