transformers pytorch bert token-classification generated_from_trainer dataset:linnaeus base_model:google-bert/bert-base-cased base_model:finetune:google-bert/bert-base-cased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-cased
datasets:
- linnaeus
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
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
model-index:
- name: bert-linnaeus-ner
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: linnaeus
      type: linnaeus
      config: linnaeus
      split: validation
      args: linnaeus
    metrics:
    - type: precision
      value: 0.9223433242506812
      name: Precision
    - type: recall
      value: 0.9521800281293952
      name: Recall
    - type: f1
      value: 0.9370242214532872
      name: F1
    - type: accuracy
      value: 0.9985110458648063
      name: Accuracy
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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
