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---
language:
- en
license: mit
base_model: distilbert-base-uncased
tags:
- low-resource NER
- token_classification
- biomedicine
- medical NER
- generated_from_trainer
datasets:
- medicine
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Dagobert42/distilbert-base-uncased-biored-augmented
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/distilbert-base-uncased-biored-augmented
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5318
- Accuracy: 0.8135
- Precision: 0.6269
- Recall: 0.5274
- F1: 0.5645
- Weighted F1: 0.803
## 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 | 0.5636 | 0.7997 | 0.7329 | 0.4936 | 0.5295 | 0.7843 |
| No log | 2.0 | 50 | 0.5561 | 0.8001 | 0.6425 | 0.5518 | 0.5689 | 0.7962 |
| No log | 3.0 | 75 | 0.5495 | 0.8093 | 0.7031 | 0.5298 | 0.568 | 0.7974 |
| No log | 4.0 | 100 | 0.5552 | 0.8036 | 0.6191 | 0.5854 | 0.5981 | 0.8002 |
| No log | 5.0 | 125 | 0.5588 | 0.8069 | 0.6268 | 0.587 | 0.6008 | 0.8032 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.15.0
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