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bert-base-multilingual-cased-ner-silvanus

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bert-base-multilingual-cased-ner-silvanus
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---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- id_nergrit_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-cased-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: 0.9068952084144917
    - name: Recall
      type: recall
      value: 0.9201581027667984
    - name: F1
      type: f1
      value: 0.9134785167745734
    - name: Accuracy
      type: accuracy
      value: 0.9851764523984384
---

<!-- 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-cased-ner-silvanus

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the id_nergrit_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0621
- Precision: 0.9069
- Recall: 0.9202
- F1: 0.9135
- Accuracy: 0.9852

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1336        | 1.0   | 827  | 0.0551          | 0.9034    | 0.9130 | 0.9082 | 0.9844   |
| 0.0461        | 2.0   | 1654 | 0.0604          | 0.9098    | 0.9134 | 0.9116 | 0.9842   |
| 0.0299        | 3.0   | 2481 | 0.0621          | 0.9069    | 0.9202 | 0.9135 | 0.9852   |


### Framework versions

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