indonesian roberta base posp tagger

Providerw11wo
Categorytoken-classification
Licensemit
Downloads2.7M
Stars0

Overview

The indonesian-roberta-base-posp-tagger is a specialized token-classification model designed for Part-of-Speech (POS) and Part-of-Speech-Plus (POSP) tagging specifically for the Indonesian language. Built on the RoBERTa architecture, it provides a robust solution for developers needing high-accuracy linguistic analysis in NLP pipelines. Unlike general-purpose LLMs, this model is optimized for precise sequence labeling, making it ideal for preprocessing tasks in sentiment analysis, named entity recognition (NER), or building grammar checkers. It integrates seamlessly into standard Hugging Face transformers workflows, offering a lightweight yet performant alternative to larger models when the primary goal is syntactic parsing of Indonesian text.

Highlights

  • Optimized for Indonesian POS and POSP token classification
  • Built on the efficient RoBERTa base architecture
  • Seamless integration with Hugging Face transformers library
  • Ideal for linguistic preprocessing and syntactic analysis
  • Permissive MIT license for commercial and open-source use

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("w11wo/indonesian-roberta-base-posp-tagger")
tokenizer = AutoTokenizer.from_pretrained("w11wo/indonesian-roberta-base-posp-tagger")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download w11wo/indonesian-roberta-base-posp-tagger

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download w11wo/indonesian-roberta-base-posp-tagger config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('w11wo/indonesian-roberta-base-posp-tagger')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/w11wo/indonesian-roberta-base-posp-tagger

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/w11wo/indonesian-roberta-base-posp-tagger

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('w11wo/indonesian-roberta-base-posp-tagger')
tokenizer = AutoTokenizer.from_pretrained('w11wo/indonesian-roberta-base-posp-tagger')

Full Documentation

来源: HuggingFace

---
license: mit
base_model: flax-community/indonesian-roberta-base
tags:

  • generated_from_trainer

datasets:
  • indonlu

language:
  • ind

metrics:
  • precision

  • recall

  • f1

  • accuracy

model-index:
  • name: indonesian-roberta-base-posp-tagger

results:
- task:
name: Token Classification
type: token-classification
dataset:
name: indonlu
type: indonlu
config: posp
split: test
args: posp
metrics:
- name: Precision
type: precision
value: 0.9625100240577386
- name: Recall
type: recall
value: 0.9625100240577386
- name: F1
type: f1
value: 0.9625100240577386
- name: Accuracy
type: accuracy
value: 0.9625100240577386
---

<!-- 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. -->

indonesian-roberta-base-posp-tagger

This model is a fine-tuned version of flax-community/indonesian-roberta-base on the indonlu dataset.
It achieves the following results on the evaluation set:

  • Loss: 0.1395

  • Precision: 0.9625

  • Recall: 0.9625

  • F1: 0.9625

  • Accuracy: 0.9625

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: 16

  • eval_batch_size: 16

  • seed: 42

  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

  • lr_scheduler_type: linear

  • num_epochs: 10

Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 420 | 0.2254 | 0.9313 | 0.9313 | 0.9313 | 0.9313 |
| 0.4398 | 2.0 | 840 | 0.1617 | 0.9499 | 0.9499 | 0.9499 | 0.9499 |
| 0.1566 | 3.0 | 1260 | 0.1431 | 0.9569 | 0.9569 | 0.9569 | 0.9569 |
| 0.103 | 4.0 | 1680 | 0.1412 | 0.9605 | 0.9605 | 0.9605 | 0.9605 |
| 0.0723 | 5.0 | 2100 | 0.1408 | 0.9635 | 0.9635 | 0.9635 | 0.9635 |
| 0.051 | 6.0 | 2520 | 0.1408 | 0.9642 | 0.9642 | 0.9642 | 0.9642 |
| 0.051 | 7.0 | 2940 | 0.1510 | 0.9635 | 0.9635 | 0.9635 | 0.9635 |
| 0.0368 | 8.0 | 3360 | 0.1653 | 0.9645 | 0.9645 | 0.9645 | 0.9645 |
| 0.0277 | 9.0 | 3780 | 0.1664 | 0.9644 | 0.9644 | 0.9644 | 0.9644 |
| 0.0231 | 10.0 | 4200 | 0.1668 | 0.9646 | 0.9646 | 0.9646 | 0.9646 |

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.2.0+cu118
  • Datasets 2.16.1
  • Tokenizers 0.15.1
View official source
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