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bert-base-multilingual-uncased-ner-silvanus

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bert-base-multilingual-uncased-ner-silvanus
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---
license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
- generated_from_trainer
datasets:
- id_nergrit_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-uncased-ner-silvanus
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: id_nergrit_corpus
      type: id_nergrit_corpus
      config: ner
      split: validation
      args: ner
    metrics:
    - name: Precision
      type: precision
      value: 0.9022118742724098
    - name: Recall
      type: recall
      value: 0.9189723320158103
    - name: F1
      type: f1
      value: 0.9105149794399845
    - name: Accuracy
      type: accuracy
      value: 0.983813651582688
---

<!-- 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-base-multilingual-uncased-ner-silvanus

This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the id_nergrit_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0662
- Precision: 0.9022
- Recall: 0.9190
- F1: 0.9105
- Accuracy: 0.9838

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1429        | 1.0   | 827  | 0.0587          | 0.8885    | 0.9075 | 0.8979 | 0.9829   |
| 0.0464        | 2.0   | 1654 | 0.0609          | 0.9081    | 0.9103 | 0.9092 | 0.9846   |
| 0.0288        | 3.0   | 2481 | 0.0662          | 0.9022    | 0.9190 | 0.9105 | 0.9838   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1