Instructions to use Wikidepia/IndoConvBERT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wikidepia/IndoConvBERT-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Wikidepia/IndoConvBERT-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Wikidepia/IndoConvBERT-base") model = AutoModel.from_pretrained("Wikidepia/IndoConvBERT-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| --- | |
| inference: false | |
| language: id | |
| # IndoConvBERT Base Model | |
| IndoConvBERT is a ConvBERT model pretrained on Indo4B. | |
| ## Pretraining details | |
| We follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90% with 128 sequence length and 10% with 512 sequence length, we pre-train the model with 512 sequence length for 1M steps on a v3-8 TPU. | |
| The current version of the model is trained on Indo4B and small Twitter dump. | |
| ## Acknowledgement | |
| Big thanks to TFRC (TensorFlow Research Cloud) for providing free TPU. | |