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---
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: 0.7632
- Accuracy: 0.7385
- Precision: 0.2012
- Recall: 0.2384
- F1: 0.215
- Weighted F1: 0.7009
## 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 | 25 | 1.2345 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 2.0 | 50 | 1.0379 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 3.0 | 75 | 1.0300 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 4.0 | 100 | 1.0228 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 5.0 | 125 | 1.0144 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 6.0 | 150 | 0.9994 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 7.0 | 175 | 0.9681 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 8.0 | 200 | 0.8869 | 0.7147 | 0.2167 | 0.1487 | 0.1303 | 0.6007 |
| No log | 9.0 | 225 | 0.8511 | 0.7242 | 0.2064 | 0.1716 | 0.1598 | 0.6298 |
| No log | 10.0 | 250 | 0.8187 | 0.7287 | 0.157 | 0.1991 | 0.1754 | 0.653 |
| No log | 11.0 | 275 | 0.8046 | 0.7317 | 0.1581 | 0.2035 | 0.1775 | 0.6581 |
| No log | 12.0 | 300 | 0.7900 | 0.732 | 0.1935 | 0.2126 | 0.1887 | 0.6688 |
| No log | 13.0 | 325 | 0.7865 | 0.734 | 0.2312 | 0.2129 | 0.1828 | 0.6664 |
| No log | 14.0 | 350 | 0.7758 | 0.7346 | 0.1604 | 0.2148 | 0.1819 | 0.6672 |
| No log | 15.0 | 375 | 0.7958 | 0.7376 | 0.2086 | 0.2141 | 0.1884 | 0.6697 |
| No log | 16.0 | 400 | 0.7757 | 0.733 | 0.2002 | 0.2347 | 0.2122 | 0.6904 |
| No log | 17.0 | 425 | 0.7874 | 0.7393 | 0.2067 | 0.2196 | 0.2119 | 0.6828 |
| No log | 18.0 | 450 | 0.7915 | 0.735 | 0.2043 | 0.2391 | 0.2197 | 0.6959 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.15.0
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