Fill-Mask
Transformers
PyTorch
TensorFlow
JAX
Safetensors
Javanese
roberta
javanese-roberta-small-imdb
Instructions to use w11wo/javanese-roberta-small-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use w11wo/javanese-roberta-small-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="w11wo/javanese-roberta-small-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("w11wo/javanese-roberta-small-imdb") model = AutoModelForMaskedLM.from_pretrained("w11wo/javanese-roberta-small-imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -14,7 +14,7 @@ Javanese RoBERTa Small IMDB is a masked language model based on the [RoBERTa mod
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The model was originally the pretrained [Javanese RoBERTa Small model](https://huggingface.co/w11wo/javanese-roberta-small) and is later fine-tuned on the Javanese IMDB movie review dataset. It achieved a perplexity of 20.83 on the validation dataset. Many of the techniques used are based on a Hugging Face tutorial [notebook](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) written by [Sylvain Gugger](https://github.com/sgugger).
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Hugging Face's `Trainer` class from the [Transformers](
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## Model
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| Model | #params | Arch. | Training/Validation data (text) |
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The model was originally the pretrained [Javanese RoBERTa Small model](https://huggingface.co/w11wo/javanese-roberta-small) and is later fine-tuned on the Javanese IMDB movie review dataset. It achieved a perplexity of 20.83 on the validation dataset. Many of the techniques used are based on a Hugging Face tutorial [notebook](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) written by [Sylvain Gugger](https://github.com/sgugger).
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Hugging Face's `Trainer` class from the [Transformers](https://huggingface.co/transformers) library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with TensorFlow nonetheless.
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## Model
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| Model | #params | Arch. | Training/Validation data (text) |
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