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mobilebert-uncased-finetuned-ner

Token Classification
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mobilebert-uncased-finetuned-ner
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- id_nergrit_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mobilebert-uncased-finetuned-ner
  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: 0.6699979179679367
    - name: Recall
      type: recall
      value: 0.6136244458216141
    - name: F1
      type: f1
      value: 0.6405732911990843
    - name: Accuracy
      type: accuracy
      value: 0.8974442203210374
---

<!-- 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. -->

# mobilebert-uncased-finetuned-ner

This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the id_nergrit_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3800
- Precision: 0.6700
- Recall: 0.6136
- F1: 0.6406
- Accuracy: 0.8974

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6239        | 1.0   | 1567 | 0.4989          | 0.5842    | 0.4877 | 0.5316 | 0.8688   |
| 0.5356        | 2.0   | 3134 | 0.4003          | 0.6368    | 0.5879 | 0.6113 | 0.8905   |
| 0.4035        | 3.0   | 4701 | 0.3800          | 0.6700    | 0.6136 | 0.6406 | 0.8974   |


### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3