transformers safetensors bert text-classification NER assertion negation it base_model:IVN-RIN/medBIT model-index autotrain_compatible endpoints_compatible region:us
---
base_model: IVN-RIN/medBIT
tags:
- bert
- NER
- assertion negation
model-index:
  - name: medbit-assertion-negation
    results:
      - task:
          type: assertion-negation
        metrics:
          - name: macro-f1
            type: macro-f1
            value: 0.946
          - name: micro-f1
            type: micro-f1
            value: 0.946
          - name: loss
            type: loss
            value: 0.417
language:
- it
widget:
- text: "Il paziente non mostra alcun segno di [entità]."
  example_title: "Negated"
- text: "Il paziente mostra chiari segni di [entità]."
  example_title: "Affirmed"
- text: "Alcuni comportamenti del paziente suggeriscono una ipotetica insorgenza di [entità]. Necessari ulteriori approfondimenti."
  example_title: "Possible"
---

# MedBIT for Clinical Assertion Negation

This model is a fine-tuned version of [IVN-RIN/medBIT-r3-plus](https://huggingface.co/IVN-RIN/medBIT-r3-plus) on a private dataset.

It achieves the following results on the evaluation set:
- Loss: 0.417
- Macro-f1: 0.946
- Micro-f1: 0.946

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 21


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
