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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-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/distilbert-base-uncased-biored-finetuned
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: 0.6868
- Accuracy: 0.7768
- Precision: 0.5392
- Recall: 0.4561
- F1: 0.4898
- Weighted F1: 0.764
## 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 | 0.9323 | 0.7124 | 0.3944 | 0.1486 | 0.1309 | 0.5993 |
| No log | 2.0 | 50 | 0.8737 | 0.7248 | 0.5187 | 0.2132 | 0.2341 | 0.6271 |
| No log | 3.0 | 75 | 0.8157 | 0.7353 | 0.4968 | 0.2886 | 0.3314 | 0.6804 |
| No log | 4.0 | 100 | 0.7927 | 0.7452 | 0.5213 | 0.3185 | 0.3686 | 0.6883 |
| No log | 5.0 | 125 | 0.7601 | 0.7507 | 0.5119 | 0.3734 | 0.4161 | 0.7116 |
| No log | 6.0 | 150 | 0.7480 | 0.7555 | 0.5381 | 0.3829 | 0.4285 | 0.718 |
| No log | 7.0 | 175 | 0.7393 | 0.7588 | 0.5393 | 0.4031 | 0.4479 | 0.7272 |
| No log | 8.0 | 200 | 0.7342 | 0.7655 | 0.5512 | 0.4143 | 0.4614 | 0.7363 |
| No log | 9.0 | 225 | 0.7391 | 0.7591 | 0.5262 | 0.4425 | 0.4709 | 0.7395 |
| No log | 10.0 | 250 | 0.7264 | 0.7644 | 0.5332 | 0.4539 | 0.4849 | 0.7484 |
| No log | 11.0 | 275 | 0.7350 | 0.7694 | 0.5419 | 0.452 | 0.4852 | 0.7483 |
| No log | 12.0 | 300 | 0.7389 | 0.77 | 0.5341 | 0.4641 | 0.4921 | 0.752 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.15.0
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