transformers pytorch roberta text-classification sequence-classification biomedical clinical nlp spanish spanish-clinical license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
tags:
- roberta
- sequence-classification
- biomedical
- clinical
- transformers
- pytorch
- nlp
- spanish
- spanish-clinical
---

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:
  
  ```python
  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)
