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jordyvl
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udpos28-sm-first-POS

Token Classification
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PyTorch
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bert
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udpos28-sm-first-POS
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- udpos28
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: udpos28-sm-first-POS
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: udpos28
      type: udpos28
      args: en
    metrics:
    - name: Precision
      type: precision
      value: 0.9511089206505667
    - name: Recall
      type: recall
      value: 0.9546093116207286
    - name: F1
      type: f1
      value: 0.9528559014062253
    - name: Accuracy
      type: accuracy
      value: 0.9559133601686793
---

<!-- 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. -->

# udpos28-sm-first-POS

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the udpos28 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1896
- Precision: 0.9511
- Recall: 0.9546
- F1: 0.9529
- Accuracy: 0.9559

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1696        | 1.0   | 4978  | 0.1700          | 0.9440    | 0.9464 | 0.9452 | 0.9472   |
| 0.0973        | 2.0   | 9956  | 0.1705          | 0.9487    | 0.9533 | 0.9510 | 0.9543   |
| 0.0508        | 3.0   | 14934 | 0.1896          | 0.9511    | 0.9546 | 0.9529 | 0.9559   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1