
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.
It is a mirror of the model available at [biodatlab/MIReAD-Neuro](https://huggingface.co/biodatlab/MIReAD-Neuro). Prefer using the model from that page for stability.
It achieves the following results on the evaluation set:
- Loss: 2.7117
- Accuracy: 0.4011
- F1: 0.3962
- Precision: 0.4066
- Recall: 0.3999


## Model description

This model was trained on a journal classification task.

## Intended uses & limitations

The intended use of this model is to create abstract embeddings for semantic similarity search. 

## Model Usage

To load the model:

```py
from transformers import BertForSequenceClassification, AutoTokenizer
model_path = "atrytone/MIReAD-Neuro"
model = BertForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
```

To create embeddings and for classification:

```py
# sample abstract & title text
title = "Why Brain Criticality Is Clinically Relevant: A Scoping Review."
abstract = "The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience..."
text = title + tokenizer.sep_token + abstract
tokens = tokenizer(
    text,
    max_length=512,
    padding=True,
    truncation=True,
    return_tensors="pt"
)

# to generate an embedding from a given title and abstract
with torch.no_grad():
  output = model.bert(**tokens)
  embedding = output.last_hidden_state[:, 0, :]

# to classify (200 journals) a given title and abstract
output = model(**tokens)
class = output.logits 
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- num_epochs: 6

{"language": ["en"], "library_name": "transformers", "metrics": ["f1", "accuracy", "recall", "precision"], "pipeline_tag": "text-classification", "widget": [{"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."}]}
transformers pytorch bert text-classification en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us 


This model is a fine-tuned version of arazd/MIReAD on a dataset of Neuroscience papers from 200 journals collected from various sources.
It is a mirror of the model available at biodatlab/MIReAD-Neuro. Prefer using the model from that page for stability.
It achieves the following results on the evaluation set:
- Loss: 2.7117
- Accuracy: 0.4011
- F1: 0.3962
- Precision: 0.4066
- Recall: 0.3999


## Model description

This model was trained on a journal classification task.

## Intended uses & limitations

The intended use of this model is to create abstract embeddings for semantic similarity search. 

## Model Usage

To load the model:



To create embeddings and for classification:



## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- num_epochs: 6

## Model description

This model was trained on a journal classification task. ## Intended uses & limitations

The intended use of this model is to create abstract embeddings for semantic similarity search. ## Model Usage

To load the model:



To create embeddings and for classification: ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- num_epochs: 6
TAGS
#transformers #pytorch #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us 
 ## Model description

This model was trained on a journal classification task. ## Intended uses & limitations

The intended use of this model is to create abstract embeddings for semantic similarity search. ## Model Usage

To load the model:



To create embeddings and for classification: ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- num_epochs: 6