Update README.md

README.md

@@ -1,199 +1,312 @@
 ---
 library_name: transformers
-tags: []
+language:
+- es
+base_model:
+- microsoft/mdeberta-v3-base
+license: cc-by-nc-4.0
+metrics:
+- accuracy
+- precision
+- recall
+- f1
 ---
 
-# Model Card for Model ID
+# Model Card for mdeberta-v3-base-re-ct
 
-<!-- Provide a quick summary of what the model is/does. -->
+This relation extraction model extracts intervention-associated relationships, temporal relations, negation/speculation and others relevant
+for clinical trials. 
 
+The model achieves the following results on the test set (when trained with the training and development set; results are averaged over 5 evaluation rounds):
+- Precision: 0.886 (±0.003)
+- Recall: 0.857 (±0.007)
+- F1: 0.869 (±0.005)
+- Accuracy: 0.911 (±0.003)
 
 
-## Model Details
+## Model description
 
-### Model Description
+This model adapts the pre-trained model [mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base). 
+It is fine-tuned to conduct relation extraction on Spanish texts about clinical trials. 
+The model is fine-tuned on the [Clinical Trials for Evidence-Based-Medicine in Spanish corpus](http://www.lllf.uam.es/ESP/nlpdata/wp2/).
 
-<!-- Provide a longer summary of what this model is. -->
+If you use this model, please, cite as follows:
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+```
+@article{campillosetal2025,
+        title = {{Benchmarking Transformer Models for Relation Extraction and Concept Normalization in a Clinical Trials Corpus}},
+        author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n and Zakhir-Puig, Sof{\'i}a and Heras-Vicente, J{\'o}nathan},
+        journal = {(Under review)},
+        year={2025}
+}
+```
 
-- **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]
+## Intended uses & limitations
 
-### Model Sources [optional]
+**Disclosure**: *This model is under development and needs to be improved. It should not be used for medical decision making without human assistance and supervision*
 
-<!-- Provide the basic links for the model. -->
+This model is intended for a generalist purpose, and may have bias and/or any other undesirable distortions.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+Third parties who deploy or provide systems and/or services using any of these models (or using systems based on these models) should note that it is their responsibility to mitigate the risks arising from their use. Third parties, in any event, need to comply with applicable regulations, including regulations concerning the use of artificial intelligence.
 
-## Uses
+The owner or creator of the models will in no event be liable for any results arising from the use made by third parties of these models.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+**Descargo de responsabilidad**: *Esta herramienta se encuentra en desarrollo y no debe ser empleada para la toma de decisiones médicas*
 
-### Direct Use
+La finalidad de este modelo es generalista, y se advierte que puede tener sesgos y/u otro tipo de distorsiones indeseables.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Terceras partes que desplieguen o proporcionen sistemas y/o servicios usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) han tener presente que es su responsabilidad abordar y minimizar los riesgos derivados de su uso. Las terceras partes, en cualquier circunstancia, deben cumplir con la normativa aplicable, incluyendo la normativa que concierne al uso de la inteligencia artificial.
 
-[More Information Needed]
+El propietario o creador de los modelos de ningún modo será responsable de los resultados derivados del uso que las terceras partes hagan de estos modelos.
 
-### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+## Training and evaluation data
 
-[More Information Needed]
+The data used for fine-tuning are the [Clinical Trials for Evidence-Based-Medicine in Spanish corpus](http://www.lllf.uam.es/ESP/nlpdata/wp2/) version 3 (annotated with semantic relationships).
+It is a collection of 1200 texts about clinical trials studies and clinical trials announcements:
+- 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO)
+- 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos
 
-### Out-of-Scope Use
+The CT-EBM-ES resource (version 1) can be cited as follows:
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+```
+@article{campillosetal-midm2021,
+        title = {A clinical trials corpus annotated with UMLS© entities to enhance the access to Evidence-Based Medicine},
+        author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n and Moreno-Sandoval, Antonio},
+        journal = {BMC Medical Informatics and Decision Making},
+        volume={21},
+        number={1},
+        pages={1--19},
+        year={2021},
+        publisher={BioMed Central}
+}
+```
 
-[More Information Needed]
 
-## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+## Training procedure
 
-[More Information Needed]
+### Training hyperparameters
 
-### Recommendations
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: we used different seeds for 5 evaluation rounds, and uploaded the model with the best results
+- optimizer: AdamW
+- weight decay: 1e-2
+- lr_scheduler_type: linear
+- num_epochs: 5 epochs.
 
-<!-- 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.
+### Training results (test set; average and standard deviation of 5 rounds with different seeds)
 
-## How to Get Started with the Model
+|   Precision    |     Recall     |       F1       |    Accuracy    |
+|:--------------:|:--------------:|:--------------:|:--------------:|
+| 0.886 (±0.003) | 0.857 (±0.007) | 0.869 (±0.005) | 0.911 (±0.003) |
 
-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]
-
-<!-- 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]
-
-## Model Card Contact
-
-[More Information Needed]
+**Results per class (test set; best model)**  
+
+|      Class      |   Precision    |     Recall     |       F1       |  Support  |
+|:---------------:|:--------------:|:--------------:|:--------------:|:---------:|
+|   Experiences    |     0.96    |    0.97    |    0.97   |     2003 | 
+|         Has_Age    |     0.93    |    0.84    |    0.88    |     152 | 
+|     Has_Dose_or_Strength    |     0.84    |    0.81   |    0.83    |     189 | 
+|            Has_Drug_Form    |     0.90    |    0.73   |     0.81    |      64 | 
+|  Has_Duration_or_Interval     |    0.83    |    0.84   |     0.84    |     365 | 
+|             Has_Frequency    |     0.79    |    0.86   |     0.82    |      84 | 
+| Has_Quantifier_or_Qualifier    |     0.91    |    0.89   |     0.90    |    1040 | 
+|        Has_Result_or_Value   |      0.92    |    0.87   |     0.89    |     384 | 
+|         Has_Route_or_Mode    |     0.91    |    0.87   |     0.89    |     221 | 
+|             Has_Time_Data    |     0.83    |    0.91   |     0.86    |     589 | 
+|              Location_of    |     0.96    |    0.96   |     0.96    |    1119 | 
+|                Used_for    |     0.89    |    0.88   |     0.89    |     731 | 
+
+### Usage
+
+
+To use this model you need to install the datasets library. 
+
+```shell
+pip install datasets
+```
+
+Then you can define the necessary functions and classes to load the model. 
+
+```python
+from transformers import (
+    DebertaV2Model, PreTrainedModel,
+    DataCollatorWithPadding,AutoTokenizer
+)
+from transformers.modeling_outputs import SequenceClassifierOutput
+import torch
+import torch.nn as nn
+from datasets import Dataset
+from torch.utils.data import DataLoader
+
+
+class DebertaV2ForRelationExtraction(PreTrainedModel):
+  def __init__(self, config, num_labels):
+    super(DebertaV2ForRelationExtraction, self).__init__(config)
+    self.num_labels = num_labels
+    # body
+    self.deberta = DebertaV2Model(config)
+    # head
+    self.dropout = nn.Dropout(config.hidden_dropout_prob)
+    self.layer_norm = nn.LayerNorm(config.hidden_size * 2)
+    self.linear = nn.Linear(config.hidden_size * 2, self.num_labels)
+    self.init_weights()
+
+  def forward(self, input_ids, token_type_ids, attention_mask,
+              span_idxs, labels=None):
+    outputs = (
+        self.deberta(input_ids, token_type_ids=token_type_ids,
+                  attention_mask=attention_mask,
+                  output_hidden_states=False)
+            .last_hidden_state)
+
+    sub_maxpool, obj_maxpool = [], []
+    for bid in range(outputs.size(0)):
+      # span includes entity markers, maxpool across span
+      sub_span = torch.max(outputs[bid, span_idxs[bid, 0]:span_idxs[bid, 1]+1, :],
+                           dim=0, keepdim=True).values
+      obj_span = torch.max(outputs[bid, span_idxs[bid, 2]:span_idxs[bid, 3]+1, :],
+                           dim=0, keepdim=True).values
+      sub_maxpool.append(sub_span)
+      obj_maxpool.append(obj_span)
+
+    sub_emb = torch.cat(sub_maxpool, dim=0)
+    obj_emb = torch.cat(obj_maxpool, dim=0)
+    rel_input = torch.cat((sub_emb, obj_emb), dim=-1)
+
+    rel_input = self.layer_norm(rel_input)
+    rel_input = self.dropout(rel_input)
+    logits = self.linear(rel_input)
+
+    if labels is not None:
+      loss_fn = nn.CrossEntropyLoss()
+      loss = loss_fn(logits.view(-1, self.num_labels), labels.view(-1))
+      return SequenceClassifierOutput(loss, logits)
+    else:
+      return SequenceClassifierOutput(None, logits)
+
+id2label = {0: 'Experiences',
+ 1: 'Has_Age',
+ 2: 'Has_Dose_or_Strength',
+ 3: 'Has_Duration_or_Interval',
+ 4: 'Has_Frequency',
+ 5: 'Has_Route_or_Mode',
+ 6: 'Location_of',
+ 7: 'Used_for'}
+
+def encode_data_inference(token_list,tokenizer):
+  tokenized_inputs = tokenizer(token_list,
+                               is_split_into_words=True,
+                               truncation=True)
+  span_idxs = []
+  for input_id in tokenized_inputs.input_ids:
+    tokens = tokenizer.convert_ids_to_tokens(input_id)
+    span_idxs.append([
+      [idx for idx, token in enumerate(tokens) if token.startswith("<S:")][0],
+      [idx for idx, token in enumerate(tokens) if token.startswith("</S:")][0],
+      [idx for idx, token in enumerate(tokens) if token.startswith("<O:")][0],
+      [idx for idx, token in enumerate(tokens) if token.startswith("</O:")][0]
+    ])
+  tokenized_inputs["span_idxs"] = span_idxs
+  # tokenized_inputs["labels"] = [label2id[label] for label in examples["label"]]
+  return tokenized_inputs
+
+def predict_example(example,model,tokenizer):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    collate_fn = DataCollatorWithPadding(tokenizer, padding="longest", return_tensors="pt")
+
+
+    encoded_data = encode_data_inference(example,tokenizer)
+
+    inferenceds = Dataset.from_dict(encoded_data)
+
+    inference_dl = DataLoader(inferenceds, 
+                         shuffle=False,
+                        #  sampler=SubsetRandomSampler(np.random.randint(0, encoded_nyt_dataset["test"].num_rows, 100).tolist()),
+                         batch_size=1, 
+                         collate_fn=collate_fn)
+    for batch in inference_dl:
+        batch = {k: v.to(device) for k, v in batch.items()}
+        with torch.no_grad():
+          outputs = model(**batch)
+          predictions = torch.argmax(outputs.logits, dim=-1).cpu().numpy()
+    return [id2label[p] for p in predictions]
+        
+```
+
+Finally, you can use it to make predictions:
+
+```python
+example = [['Título',
+  'público:',
+  'Estudio',
+  'multicéntrico,',
+  'aleatorizado,',
+  'doble',
+  'ciego,',
+  'controlado',
+  'con',
+  'placebo',
+  'del',
+  'anticuerpo',
+  'monoclonal',
+  'humano',
+  'anti-TNF',
+  'Adalimumab',
+  'en',
+  '<S:LIV>',
+  'sujetos',
+  'pediátricos',
+  '</S:LIV>',
+  'con',
+  'colitis',
+  'ulcerosa',
+  'moderada',
+  'o',
+  'grav<O:CHE>',
+  'Adalimumab',
+  '</O:CHE>blico:',
+  'Estudio',
+  'multicéntrico,',
+  'aleatorizado,',
+  'doble',
+  'ciego,',
+  'controlado',
+  'con',
+  'placebo',
+  'del',
+  'anticuerpo',
+  'monoclonal',
+  'humano',
+  'anti-TNF',
+  'Adalimumab',
+  'en',
+  'sujetos',
+  'pediátricos',
+  'con',
+  'colitis',
+  'ulcerosa',
+  'moderada',
+  'o',
+  'grave']]
+
+model = DebertaV2ForRelationExtraction.from_pretrained("medspaner/mdeberta-v3-base-re-ct-v2",8)  
+tokenizer = AutoTokenizer.from_pretrained("medspaner/mdeberta-v3-base-re-ct-v2")
+predict_example(example,model,tokenizer)
+```
+
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.0.1+cu117
+- Datasets 2.15.0
+- Tokenizers 0.19.1