File size: 1,800 Bytes
1c1c1e9 d1d549f 1c1c1e9 770bbf1 1c1c1e9 770bbf1 1c1c1e9 770bbf1 1c1c1e9 770bbf1 1c1c1e9 770bbf1 1c1c1e9 c26d54c 1c1c1e9 c26d54c 1c1c1e9 c26d54c 1c1c1e9 770bbf1 0625f74 770bbf1 2df62c1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Sultannn/bert-base-ft-ner-xtreme-id
results: []
language: id
datasets:
- xtreme
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Sultannn/bert-base-ft-ner-xtreme-id
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an [xtreme PAN-X.id](https://huggingface.co/datasets/xtreme/viewer/PAN-X.id/test) for **NER** downstream task.
## Details of the downstream task (NER) - Dataset
| Dataset | # Examples |
| ---------------------- | ----- |
| Train | 35 K |
| Validation | 5 K |
## Metrics on evaluation set
| Metrics | Score |
| ---------------------- | ----- |
| Accuracy | **97.18** |
| F1 | **93.26** |
| Precision | **92.36** |
| Recall | **94.18** |
## Training hyperparameters
- Optimizer = AdamW
- LearningRate = 4e-5
- WeightDecay = 1e-2
- Warmup = 500
## Example of usage
```python
# pipeline example
from transformers import pipeline
model_checkpoint = "Sultannn/bert-base-ft-ner-xtreme-id"
token_classifier = pipeline(
"token-classification", model=model_checkpoint, aggregation_strategy="simple")
text = "nama saya Tono saya bekerja di Facebook dan tinggal di Jawa"
token_classifier(text)
```
## Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
> [Fine-tune on NER script provided by @Sultan](https://github.com/sultanbst123)
> Made with <span style="color: #e25555;">♥</span> in 🌏
|