transformers safetensors mobilebert token-classification low-resource NER token_classification biomedicine medical NER generated_from_trainer en dataset:medicine base_model:mobilebert-uncased license:mit autotrain_compatible endpoints_compatible region:us
---
language:
- en
license: mit
base_model: mobilebert-uncased
tags:
- low-resource NER
- token_classification
- biomedicine
- medical NER
- generated_from_trainer
datasets:
- medicine
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Dagobert42/mobilebert-uncased-biored-finetuned
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Dagobert42/mobilebert-uncased-biored-finetuned

This model is a fine-tuned version of [mobilebert-uncased](https://huggingface.co/mobilebert-uncased) on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0264
- Accuracy: 0.7163
- Precision: 0.1023
- Recall: 0.1429
- F1: 0.1192
- Weighted F1: 0.5979

## 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: 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Weighted F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
| No log        | 1.0   | 13   | 1.6911          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 2.0   | 26   | 1.2080          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 3.0   | 39   | 1.0905          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 4.0   | 52   | 1.0400          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 5.0   | 65   | 1.0439          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 6.0   | 78   | 1.0288          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 7.0   | 91   | 1.0259          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 8.0   | 104  | 1.0180          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 9.0   | 117  | 1.0229          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 10.0  | 130  | 1.0186          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.15.0
