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--- |
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language: |
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- en |
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pipeline_tag: text-classification |
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metrics: |
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- f1 |
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- accuracy |
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- recall |
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- precision |
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library_name: transformers |
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widget: |
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- text: The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience. The potential of criticality to explain various brain properties, including optimal information processing, has made it an increasingly exciting area of investigation for neuroscientists. Recent reviews on this topic, sometimes termed brain criticality, make brief mention of clinical applications of these findings to several neurological disorders such as epilepsy, neurodegenerative disease, and neonatal hypoxia. Other clinicallyrelevant domains - including anesthesia, sleep medicine, developmental-behavioral pediatrics, and psychiatry - are seldom discussed in review papers of brain criticality. Thorough assessments of these application areas and their relevance for clinicians have also yet to be published. In this scoping review, studies of brain criticality involving human data of all ages are evaluated for their current and future clinical relevance. To make the results of these studies understandable to a more clinical audience, a review of the key concepts behind criticality (e.g., phase transitions, long-range temporal correlation, self-organized criticality, power laws, branching processes) precedes the discussion of human clinical studies. Open questions and forthcoming areas of investigation are also considered. |
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--- |
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# MIReAD Neuro |
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This model is a fine-tuned version of [arazd/MIReAD](https://huggingface.co/arazd/MIReAD) on a dataset of Neuroscience papers from 200 journals collected from various sources for a journal classification task. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.7117 |
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- Accuracy: 0.4011 |
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- F1: 0.3962 |
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- Precision: 0.4066 |
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- Recall: 0.3999 |
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## Model description |
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This model was trained on a journal classification task. |
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## Intended uses & limitations |
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The intended use of this model is to create abstract embeddings for semantic similarity search for neuroscience-related articles. |
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## Model Usage |
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To load the model: |
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```py |
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from transformers import BertForSequenceClassification, AutoTokenizer |
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model_path = "biodatlab/MIReAD-Neuro" |
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model = BertForSequenceClassification.from_pretrained(model_path) |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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``` |
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To create embeddings and for classification: |
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```py |
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# sample abstract & title text |
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title = "Why Brain Criticality Is Clinically Relevant: A Scoping Review." |
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abstract = "The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience..." |
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text = title + tokenizer.sep_token + abstract |
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tokens = tokenizer( |
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text, |
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max_length=512, |
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padding=True, |
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truncation=True, |
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return_tensors="pt" |
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) |
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# to generate an embedding from a given title and abstract |
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with torch.no_grad(): |
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output = model.bert(**tokens) |
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embedding = output.last_hidden_state[:, 0, :] |
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# to classify (200 journals) a given title and abstract |
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output = model(**tokens) |
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class = output.logits |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- num_epochs: 6 |
