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---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
- Multilingual
model-index:
- name: bert-base-multilingual-cased-fine_tuned-ner-WikiNeural_Multilingual
results: []
datasets:
- dmargutierrez/Babelscape-wikineural-joined
language:
- en
- es
- nl
- fr
- it
- ru
- de
- pt
- pl
metrics:
- seqeval
pipeline_tag: token-classification
---
# bert-base-multilingual-cased-fine_tuned-ner-WikiNeural_Multilingual
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased).
It achieves the following results on the evaluation set:
- Loss: 0.0168
- Overall
- Precision: 0.9957
- Recall: 0.9961
- F1: 0.9959
- Accuracy: 0.9947
- Loc
- Precision: 0.9983410191680872
- Recall: 0.99820099576644
- F1: 0.9982710025571356
- Number: 1,932,180
- Misc
- Precision: 0.9809396911027518
- Recall: 0.9833044214778437
- F1: 0.9821206328547606
- Number: 122,787
- Org
- Precision: 0.9868919798954698
- Recall: 0.9881129520338388
- F1: 0.9875020885547201
- Number: 59,813
- Per
- Precision: 0.9386096837531854
- Recall: 0.9516901050491359
- F1: 0.9451046377116415
- Number: 47,216
## Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Token%20Classification/Multilingual/Babelscape-WikiNeural-Joined%20Dataset/Babelscape%20WikiNeural%20Joined%20Dataset%20With%20Multilingual%20BERT.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril.
## Training and evaluation data
Dataset Soruce: https://huggingface.co/datasets/dmargutierrez/Babelscape-wikineural-joined
## 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: 1
### Training results
| Train Loss | Epoch | Step | Valid Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
| 0.015 | 1.0 | 102700 | 0.0168 | 0.9957 | 0.9961 | 0.9959 | 0.9947 |
| Train Loss | Epoch | Valid Loss | LOC Precision | LOC Recall | LOC F1 | LOC Number | MISC Precision | MISC Recall | MISC F1 | MISC Number | ORG Precision | ORG Recall | ORG F1 | ORG Number | PER Precision | PER Recall | PER F1 | PER Number |
|:-------------:|:-----:|:---------------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-------------:|:-------:|:---------------:|
| 0.015 | 1.0 | 0.0168 | 0.9983 | 0.9982 | 0.9983 | 1,932,180 | 0.9809 | 0.9833 | 0.9821 | 122,787 | 0.9869 | 0.9881 | 0.9875 | 59,813 | 0.9386 | 0.9517 | 0.9451 | 47,216 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3 |