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Cahya

Rank:

Average Model Cost: $0.0000

Number of Runs: 16,296

Models by this creator

xlm-roberta-large-indonesian-NER

Platform did not provide a description for this model.

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3.1K

Huggingface

🐍

bert-base-indonesian-522M

It is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between indonesia and Indonesia. This is one of several other language models that have been pre-trained with indonesian datasets. More detail about its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: and in Tensorflow: This model was pre-trained with 522MB of indonesian Wikipedia. The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are then of the form: [CLS] Sentence A [SEP] Sentence B [SEP]

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3.1K

Huggingface

🤖

bert-base-indonesian-NER

Platform did not provide a description for this model.

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$-/run

3.0K

Huggingface

🤯

distilbert-base-indonesian

This model is a distilled version of the Indonesian BERT base model. This model is uncased. This is one of several other language models that have been pre-trained with indonesian datasets. More detail about its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: and in Tensorflow: This model was distiled with 522MB of indonesian Wikipedia and 1GB of indonesian newspapers. The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are then of the form: [CLS] Sentence A [SEP] Sentence B [SEP]

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2.5K

Huggingface

🌿

roberta-base-indonesian-522M

It is RoBERTa-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between indonesia and Indonesia. This is one of several other language models that have been pre-trained with indonesian datasets. More detail about its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: and in Tensorflow: This model was pre-trained with 522MB of indonesian Wikipedia. The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are then of the form: <s> Sentence A </s> Sentence B </s>

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2.5K

Huggingface

👁️

bert-base-indonesian-1.5G

Platform did not provide a description for this model.

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$-/run

709

Huggingface

🎯

t5-base-indonesian-summarization-cased

Finetuned T5 base summarization model for Indonesian. t5-base-indonesian-summarization-cased model is based on t5-base-bahasa-summarization-cased by huseinzol05, finetuned using id_liputan6 dataset. Output:

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475

Huggingface

📶

gpt2-small-indonesian-522M

It is GPT2-small model pre-trained with indonesian Wikipedia using a causal language modeling (CLM) objective. This model is uncased: it does not make a difference between indonesia and Indonesia. This is one of several other language models that have been pre-trained with indonesian datasets. More detail about its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: Here is how to use this model to get the features of a given text in PyTorch: and in Tensorflow: This model was pre-trained with 522MB of indonesian Wikipedia. The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 52,000. The inputs are sequences of 128 consecutive tokens.

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360

Huggingface

gpt2-large-indonesian-522M

Platform did not provide a description for this model.

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$-/run

281

Huggingface

🚀

whisper-medium-id

Whisper Medium Indonesian This model is a fine-tuned version of openai/whisper-medium on the Indonesian mozilla-foundation/common_voice_11_0, magic_data, titml and google/fleurs dataset. It achieves the following results: CV11 test split: Loss: 0.0698 Wer: 3.8274 Google/fleurs test split: Wer: 9.74 Usage 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: 1e-06 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 lr_scheduler_warmup_steps: 500 training_steps: 10000 mixed_precision_training: Native AMP Training results Evaluation We evaluated the model using the test split of two datasets, the Common Voice 11 and the Google Fleurs. As Whisper can transcribe casing and punctuation, we also evaluate its performance using raw and normalized text. (lowercase + removal of punctuations). The results are as follows: Common Voice 11 Google/Fleurs Framework versions Transformers 4.26.0.dev0 Pytorch 1.13.0+cu117 Datasets 2.7.1.dev0 Tokenizers 0.13.2

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236

Huggingface

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