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---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- linnaeus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-linnaeus-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: linnaeus
type: linnaeus
config: linnaeus
split: validation
args: linnaeus
metrics:
- name: Precision
type: precision
value: 0.9223433242506812
- name: Recall
type: recall
value: 0.9521800281293952
- name: F1
type: f1
value: 0.9370242214532872
- name: Accuracy
type: accuracy
value: 0.9985110458648063
widget:
- text: "Streptococcus suis (S. suis) is an important zoonosis and pathogen that can carry prophages."
- text: "Lactobacillus plantarum is an important probiotic and is mostly isolated from fermented foods."
inference:
parameters:
aggregation_strategy: "first"
---
<!-- 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. -->
# bert-linnaeus-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the linnaeus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0073
- Precision: 0.9223
- Recall: 0.9522
- F1: 0.9370
- Accuracy: 0.9985
## Model description
This model can be used to find organisms and species in text data.
NB. THIS MODEL IS WIP AND IS SUBJECT TO CHANGE!
## Intended uses & limitations
This model's intended use is in my Master's thesis to mask names of bacteria (and phages) for further analysis.
## Training and evaluation data
Linnaeus dataset was used to train and validate the performance.
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0076 | 1.0 | 1492 | 0.0128 | 0.8566 | 0.9578 | 0.9044 | 0.9967 |
| 0.0024 | 2.0 | 2984 | 0.0082 | 0.9092 | 0.9578 | 0.9329 | 0.9980 |
| 0.0007 | 3.0 | 4476 | 0.0073 | 0.9223 | 0.9522 | 0.9370 | 0.9985 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.0
|