ELiRF/Chest-COVID

model_name: roberta-base-biomedical-clinical model_type: roberta model_size: base pretrained_model_name_or_path: roberta-base

Model description

Replace with a concise description of the model's purpose

description: | This is a custom RoBERTa model fine-tuned on a biomedical/clinical dataset for sequence classification tasks. It is designed to handle text within the biomedical and clinical domains, making it suitable for tasks like document classification and sentence-level classification. The model has been pre-trained on general text and then fine-tuned on a specific biomedical/clinical corpus to capture domain-specific patterns.

Intended Use

Replace with information about the specific tasks the model is intended for

intended_use: | This model can be used for sequence classification tasks in the biomedical and clinical domain, such as medical text classification, sentiment analysis, or named entity recognition in clinical texts. It is well-suited for tasks that involve understanding medical terminology and biomedical context.

Model Details

A brief description of the model's architecture, what it has been fine-tuned for, and important details

model_details: | This RoBERTa model is based on the original architecture and consists of 12 transformer layers, each with a hidden size of 768 and a feed-forward layer size of 3072. It was fine-tuned on a specialized biomedical and clinical corpus to enhance its performance in domain-specific tasks. The model accepts sequences of up to 512 tokens.

Training data

Describe where the training data comes from

training_data: | The model was fine-tuned on a biomedical/clinical dataset. This corpus includes medical literature, clinical notes, and other biomedical texts that provide a rich source of domain-specific language.

Evaluation results

If available, include performance metrics such as accuracy, F1 score, etc.

evaluation_results: | Evaluation results can vary depending on the task and dataset used for testing. For this model, typical evaluation metrics (e.g., accuracy, F1 score) should be reported based on a specialized biomedical/clinical benchmark.

Usage instructions

How to use the model

usage_instructions: | To use the model, simply load it with the transformers library:

from transformers import RobertaTokenizer, RobertaForSequenceClassification

model_name = "roberta-base-biomedical-clinical"
tokenizer = RobertaTokenizer.from_pretrained(model_name)
model = RobertaForSequenceClassification.from_pretrained(model_name)

inputs = tokenizer("your input text here", return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)