Update README.md

README.md

@@ -1,63 +1,79 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- biology
+- medical
+license: cc-by-nc-nd-4.0
+datasets:
+- fundacionctic/DermatES
+language:
+- es
+metrics:
+- accuracy
+- f1
+pipeline_tag: text-classification
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
+This is a fine-tuned version of the pre-trained biomedical language model [bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) in Spanish, tailored for text classification tasks. We used two NVIDIA GPUs for training.
 
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This model has been fine-tuned for text classification on dermatological Spanish electronic health records (EHR). It leverages the pre-trained biomedical language understanding from the [bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) model and adapts it to classify dermatology-related texts effectively.
+The model is intended to predict among 25 different skin diseases from a medical record. It could be a first visit or a follow-up visit.
+It takes as input four features: 
+- *textual medical record:* the EHR written by a doctor
+- *disease type:* the type of disease associated with the EHR
+- *disease location:* the location in the body of the disease
+- *disease severity:* how severe or lethal is the disease 
+It is IMPORTANT to load and concatenate them in this specific order.
+The details to reproduce the cascade predictions are available in the Training section.
 
 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]
+- **Developed by:** [Fundacion CTIC](https://www.fundacionctic.org)
+- **Funded by [optional]:** [SATEC](https://www.satec.es)
+- **Model type:** Fine-tuned LM Encoder
+- **Language(s) (NLP):** Spanish
+- **License:** CC-BY-NC
+- **Finetuned from model [optional]:** [bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es)
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
+- **Repository:** 
+- **Paper [optional]:** Coming soon...
 - **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. -->
-
+The Model is industry-friendly and the best model of the **dermat** collection. The vanilla version of the model is called [vanilla-dermat](https://huggingface.co/fundacionctic/vanilla-dermat/) and is meant to predict not only the disease but also the 3 features mentionned above. 
+We DO NOT recommend to fine-tune this model. It is already meant to be a downstream task.
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model can be directly used for classifying dermatological text data in Spanish EHRs.
 
-[More Information Needed]
 
 ### Downstream Use [optional]
+The model can be integrated into healthcare applications for automatic classification of dermatological conditions from patient records.
 
-<!-- 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. -->
+The model is not suitable for non-medical text classification tasks or for texts in languages other than Spanish.
 
 [More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+This model is fine-tuned on a specific dataset and may not generalize well to other types of medical texts or conditions. Users should be cautious of biases in the training data that could affect the model's performance.
 
 [More Information Needed]
 
@@ -65,11 +81,26 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- 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.
+Users should validate the model's performance on their specific data and consider any ethical implications of deploying a machine learning model in a healthcare setting.
 
 ## How to Get Started with the Model
+```
+from transformers import RobertaTokenizerFast, RobertaForSequenceClassification,
+
+tokenizer = RobertaTokenizerFast.from_pretrained("fundacionctic/predict-dermat")
+model = RobertaForSequenceClassification.from_pretrained("fundacionctic/predict-dermat")
+
+
+inputs = tokenizer("Ejemplo de texto dermatológico + tipo + localizacion + gravedad".tolist(),
+            truncation=True,
+            padding='max_length',
+            max_length=max_length,  # Replace with your desired maximum sequence length
+            return_tensors='pt',
+            return_attention_mask=True,
+        ))
+outputs = model(input_ids, attention_mask=attention_mask)
+```
 
-Use the code below to get started with the model.
 
 [More Information Needed]
 
@@ -77,38 +108,71 @@ Use the code below to get started with the model.
 
 ### 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. -->
-
+The model was fine-tuned on the DermatES dataset from Fundación CTIC, which contains Spanish dermatological EHRs.
 [More Information Needed]
 
 ### Training Procedure
+In order to reproduce the experiment it is ESSENTIAL to respect the order of prediction of the three ontology-base features. More details in the original paper of *Dermat*
+
+```
+from transformers import RobertaTokenizerFast, RobertaForSequenceClassification,
+
+tokenizer = RobertaTokenizerFast.from_pretrained("PlanTL-GOB-ES/bsc-bio-ehr-es")
+model = RobertaForSequenceClassification.from_pretrained("PlanTL-GOB-ES/bsc-bio-ehr-es")
+
+def reset_model():
+  model =  RobertaForSequenceClassification.from_pretrained("PlanTL-GOB-ES/bsc-bio-ehr-es")
+
+def cascade(inputs,information_list,model,tokenizer,predictions=None):
+  if not information_list:
+    return predictions
+  else : 
+    inputs = tokenizer("Ejemplo de texto dermatológico".tolist(),
+                truncation=True,
+                padding='max_length',
+                max_length=max_length,  # Replace with your desired maximum sequence length
+                return_tensors='pt',
+                return_attention_mask=True,
+            ))
+    labels = information_list[0].tolist()
+    outputs = model(input_ids, attention_mask=attention_mask,labels=labels)
+    predictions = torch.argmax(outputs.logits,dim=1)
+    inputs = [tokenizer.decode(input+predictions[i]) for i,input in enumerate(inputs)]
+    model = reset_model()
+    return cascade(inputs,information_list[1:],model,tokenizer,predictions)
+inputs = ["un informe,","otro informe"]
+information_list = [[tipo1,tipo2],[sitio1,sitio2],[gravedad1,gravedad2]]
+predicted_diseases = cascade(inputs,information_list,model,tokenizer)
+```
 
-<!-- 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]
+Lowercased, anonymized and accents removed texts
 
 
 #### 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 -->
+- **Training regime:** fp32
 
 #### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+    Epochs: 7
+    Batch size: 64
+    Learning rate: 0.0001
 
 [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. -->
+The evaluation was performed on 0.2 of the [DermatES](https://huggingface.co/datasets/fundacionctic/DermatES) dataset.
 
 [More Information Needed]
 
@@ -120,17 +184,15 @@ Use the code below to get started with the model.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+    - *Accuracy:* 0.51
+    - *F1 Score:* 0.42
+    - *top-k (k=2) accuracy:* 0.67
+    - *top-k (k=2) f1 Score:* 0.61
 
-### Results
-
-[More Information Needed]
 
 #### Summary
 
-
+The model achieves poor accuracy and F1 score on dermatological text classification, demonstrating the need of using ontologies (see [oracle-dermat](https://huggingface.co/datasets/fundacionctic/oracle-dermat) ) for this specific medical domain.
 
 ## Model Examination [optional]
 
@@ -144,35 +206,34 @@ Use the code below to get started with the model.
 
 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]
+- **Hardware Type:** GPU and CPU with avx
+- **Hours used:** >96
 - **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
+- **Compute Region:** EU
 - **Carbon Emitted:** [More Information Needed]
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model is based on the [RoBERTa](https://huggingface.co/FacebookAI/roberta-base) architecture, fine-tuned for the objective of text classification in the biomedical domain.
 
 ### Compute Infrastructure
 
-[More Information Needed]
 
-#### Hardware
 
-[More Information Needed]
+#### Hardware
 
+Two NVIDIA GPUs were used for the fine-tuning process.
 #### Software
 
-[More Information Needed]
+The fine-tuning was performed using the 🤗 Transformers library.
 
 ## 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:**
+**BibTeX:** Coming soon 
 
 [More Information Needed]
 
@@ -182,9 +243,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## 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]
 
@@ -192,8 +251,8 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+Leon-Paul Schaub Torre, Pelayo Quiros and Helena Garcia-Mieres
 
 ## Model Card Contact
 
-[More Information Needed]
+leon.schaub@fundacionctic.org