Deid Roberta I2b2 NER Medical Reports

🚀 fine-tuned-model

This is a fine-tuned model based on obi/deid_roberta_i2b2, achieving high performance on specific evaluation metrics.

📦 Installation

The model is part of the transformers library. You can install it using the following command:

pip install transformers

💻 Usage Examples

Basic Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "your_model_path"  # Replace with the actual model path
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

input_text = "Your input text here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model(**inputs)

📚 Documentation

Model description

This model is a fine-tuned version of obi/deid_roberta_i2b2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2126
• Model Preparation Time: 0.0061
• Precision: 0.9143
• Recall: 0.9156
• F1: 0.9132
• Accuracy: 0.9156

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu124
• Datasets 2.14.5
• Tokenizers 0.19.1

📄 License

This project is licensed under the MIT license.