# Komunitas

Halaman ini menghimpun sumber daya seputar 🤗 Transformers yang dikembangkan oleh komunitas.

## Sumber daya komunitas

| Sumber daya     |      Deskripsi      |      Penulis      |
|:----------|:-------------|------:|
| [Kartu Kilat Glosarium Hugging Face Transformers](https://www.darigovresearch.com/huggingface-transformers-glossary-flashcards) | Seperangkat kartu flash berdasarkan [Glosarium Dokumentasi Transformers](glossary) yang telah disusun dalam format yang mudah dipelajari/direvisi menggunakan [Anki](https://apps.ankiweb.net/), aplikasi open source lintas platform yang dirancang khusus untuk retensi pengetahuan jangka panjang. Lihat [video pengantar tentang cara menggunakan kartu flash ini](https://www.youtube.com/watch?v=Dji_h7PILrw). | [Darigov Research](https://www.darigovresearch.com/) |

## Notebook komunitas

| Notebook     |      Deskripsi      |      Penulis      |      |
|:----------|:-------------|:-------------|------:|
| [Menyesuaikan Transformer yang telah dilatih sebelumnya untuk menghasilkan lirik](https://github.com/AlekseyKorshuk/huggingartists) | Cara membuat lirik lagu dengan gaya artis favorit Anda dengan melakukan fine-tuning pada model GPT-2 |  [Aleksey Korshuk](https://github.com/AlekseyKorshuk) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb) |
| [Melatih T5 di TPU](https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb)  | Cara melatih T5 pada SQUAD dengan Transformers dan Nlp | [Suraj Patil](https://github.com/patil-suraj) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb#scrollTo=QLGiFCDqvuil) |
| [Menyesuaikan T5 untuk Klasifikasi dan Pilihan Ganda](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb)  | Cara melakukan fine-tuning T5 untuk tugas klasifikasi dan pilihan ganda menggunakan format teks-ke-teks dengan PyTorch Lightning |  [Suraj Patil](https://github.com/patil-suraj) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) |
| [Menyesuaikan DialoGPT pada Dataset dan Bahasa Baru](https://github.com/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb)  | Cara melakukan fine-tuning model DialoGPT pada dataset baru untuk chatbot percakapan open-dialog |  [Nathan Cooper](https://github.com/ncoop57) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb) |
| [Modeling Urutan Panjang dengan Reformer](https://github.com/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb)  | Cara melatih pada sekuens sepanjang 500.000 token dengan Reformer |  [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb)  |
| [Menyesuaikan BART untuk Ringkasan](https://github.com/ohmeow/ohmeow_website/blob/master/posts/2021-05-25-mbart-sequence-classification-with-blurr.ipynb) | Cara melakukan fine-tuning BART untuk summarization dengan fastai menggunakan blurr | [Wayde Gilliam](https://ohmeow.com/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ohmeow/ohmeow_website/blob/master/posts/2021-05-25-mbart-sequence-classification-with-blurr.ipynb) |
| [Latih ulang Transformer yang sudah dilatih sebelumnya pada tweet siapa pun](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb) | Cara membuat cuitan bergaya akun Twitter favorit Anda dengan fine-tuning model GPT-2 |  [Boris Dayma](https://github.com/borisdayma) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb) |
| [Optimalkan model 🤗 Hugging Face dengan Weights & Biases](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/huggingface/Optimize_Hugging_Face_models_with_Weights_%26_Biases.ipynb) | Tutorial lengkap yang menampilkan integrasi W&B dengan Hugging Face | [Boris Dayma](https://github.com/borisdayma) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/huggingface/Optimize_Hugging_Face_models_with_Weights_%26_Biases.ipynb) |
| [Pretrain Longformer](https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb)  | Cara membuat versi "panjang" dari model pretrained yang sudah ada |  [Iz Beltagy](https://beltagy.net) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb) |
| [Menyesuaikan Longformer untuk QA](https://github.com/patil-suraj/Notebooks/blob/master/longformer_qa_training.ipynb) | Cara melakukan fine-tuning model longformer untuk tugas QA | [Suraj Patil](https://github.com/patil-suraj) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/Notebooks/blob/master/longformer_qa_training.ipynb) |
| [Evaluasi Model dengan 🤗nlp](https://github.com/patrickvonplaten/notebooks/blob/master/How_to_evaluate_Longformer_on_TriviaQA_using_NLP.ipynb) | Cara mengevaluasi longformer pada TriviaQA dengan `nlp` | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1m7eTGlPmLRgoPkkA7rkhQdZ9ydpmsdLE?usp=sharing) |
| [Menyesuaikan T5 untuk Ekstraksi Rentang Sentimen](https://github.com/enzoampil/t5-intro/blob/master/t5_qa_training_pytorch_span_extraction.ipynb)  | Cara melakukan fine-tuning T5 untuk ekstraksi span sentimen menggunakan format teks-ke-teks dengan PyTorch Lightning |  [Lorenzo Ampil](https://github.com/enzoampil) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/enzoampil/t5-intro/blob/master/t5_qa_training_pytorch_span_extraction.ipynb) |
| [Menyesuaikan DistilBert untuk Klasifikasi Multikelas](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_multiclass_classification.ipynb) | Cara melakukan fine-tuning DistilBert untuk klasifikasi multikelas dengan PyTorch | [Abhishek Kumar Mishra](https://github.com/abhimishra91) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_multiclass_classification.ipynb)|
|[Menyesuaikan BERT untuk Klasifikasi Multi-label](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_multi_label_classification.ipynb)|Cara melakukan fine-tuning BERT untuk klasifikasi multi-label menggunakan PyTorch|[Abhishek Kumar Mishra](https://github.com/abhimishra91) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_multi_label_classification.ipynb)|
|[Menyesuaikan T5 untuk Ringkasan](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb)|Cara melakukan fine-tuning T5 untuk summarization di PyTorch dan melacak eksperimen dengan WandB|[Abhishek Kumar Mishra](https://github.com/abhimishra91) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb)|
|[Percepat Fine-Tuning di Transformers dengan Dynamic Padding / Bucketing](https://github.com/ELS-RD/transformers-notebook/blob/master/Divide_Hugging_Face_Transformers_training_time_by_2_or_more.ipynb)|Cara mempercepat fine-tuning hingga 2 kali lipat menggunakan dynamic padding / bucketing|[Michael Benesty](https://github.com/pommedeterresautee) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CBfRU1zbfu7-ijiOqAAQUA-RJaxfcJoO?usp=sharing)|
|[Pretrain Reformer untuk Masked Language Modeling](https://github.com/patrickvonplaten/notebooks/blob/master/Reformer_For_Masked_LM.ipynb)| Cara melatih model Reformer dengan lapisan self-attention bi-directional | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tzzh0i8PgDQGV3SMFUGxM7_gGae3K-uW?usp=sharing)|
|[Perluas dan Sesuaikan Sci-BERT](https://github.com/lordtt13/word-embeddings/blob/master/COVID-19%20Research%20Data/COVID-SciBERT.ipynb)| Cara meningkatkan kosakata model SciBERT pretrained dari AllenAI pada dataset CORD dan memipelinnya. | [Tanmay Thakur](https://github.com/lordtt13) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1rqAR40goxbAfez1xvF3hBJphSCsvXmh8)|
|[Menyesuaikan BlenderBotSmall untuk Ringkasan menggunakan API Trainer](https://github.com/lordtt13/transformers-experiments/blob/master/Custom%20Tasks/fine-tune-blenderbot_small-for-summarization.ipynb)| Cara melakukan fine-tuning BlenderBotSmall untuk ringkasan pada dataset kustom, menggunakan Trainer API. | [Tanmay Thakur](https://github.com/lordtt13) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/19Wmupuls7mykSGyRN_Qo6lPQhgp56ymq?usp=sharing)|
|[Menyesuaikan Electra dan menginterpretasikan dengan Integrated Gradients](https://github.com/elsanns/xai-nlp-notebooks/blob/master/electra_fine_tune_interpret_captum_ig.ipynb) | Cara melakukan fine-tuning Electra untuk analisis sentimen dan menafsirkan prediksi dengan Captum Integrated Gradients | [Eliza Szczechla](https://elsanns.github.io) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/elsanns/xai-nlp-notebooks/blob/master/electra_fine_tune_interpret_captum_ig.ipynb)|
|[melakukan fine-tuning pada Model GPT-2 non-Bahasa Inggris dengan kelas Trainer](https://github.com/philschmid/fine-tune-GPT-2/blob/master/Fine_tune_a_non_English_GPT_2_Model_with_Huggingface.ipynb) | Cara melakukan fine-tuning model GPT-2 non-Inggris dengan kelas Trainer | [Philipp Schmid](https://www.philschmid.de) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/philschmid/fine-tune-GPT-2/blob/master/Fine_tune_a_non_English_GPT_2_Model_with_Huggingface.ipynb)|
|[Menyesuaikan Model DistilBERT untuk tugas Klasifikasi Multi Label](https://github.com/DhavalTaunk08/Transformers_scripts/blob/master/Transformers_multilabel_distilbert.ipynb) | Cara melakukan fine-tuning Model DistilBERT untuk tugas Klasifikasi Multi Label | [Dhaval Taunk](https://github.com/DhavalTaunk08) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhavalTaunk08/Transformers_scripts/blob/master/Transformers_multilabel_distilbert.ipynb)|
|[Menyesuaikan ALBERT untuk klasifikasi pasangan kalimat](https://github.com/NadirEM/nlp-notebooks/blob/master/Fine_tune_ALBERT_sentence_pair_classification.ipynb) | Cara melakukan fine-tuning model ALBERT atau model berbasis BERT lainnya untuk tugas klasifikasi pasangan kalimat | [Nadir El Manouzi](https://github.com/NadirEM) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NadirEM/nlp-notebooks/blob/master/Fine_tune_ALBERT_sentence_pair_classification.ipynb)|
|[Menyesuaikan ulang Roberta untuk analisis sentimen](https://github.com/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb) | Cara melakukan fine-tuning model Roberta untuk analisis sentimen | [Dhaval Taunk](https://github.com/DhavalTaunk08) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb)|
|[Mengevaluasi Model Pembentukan Pertanyaan](https://github.com/flexudy-pipe/qugeev) | Seberapa akurat jawaban atas pertanyaan yang dihasilkan oleh model transformer seq2seq Anda? | [Pascal Zoleko](https://github.com/zolekode) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bpsSqCQU-iw_5nNoRm_crPq6FRuJthq_?usp=sharing)|
|[Memanfaatkan BERT untuk Ringkasan Encoder-Decoder pada CNN/Dailymail](https://github.com/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb) | Cara memulai *EncoderDecoderModel* dengan checkpoint *google-bert/bert-base-uncased* untuk ringkasan pada CNN/Dailymail | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb)|
|[Memanfaatkan RoBERTa untuk Ringkasan Encoder-Decoder pada BBC XSum](https://github.com/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb) | Cara memulai dengan hangat (warm-start) *EncoderDecoderModel* bersama menggunakan checkpoint *FacebookAI/roberta-base* untuk ringkasan pada BBC/XSum | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb)|
|[Menyesuaikan TAPAS pada Sequential Question Answering (SQA)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) | Cara melakukan fine-tuning *TapasForQuestionAnswering* dengan checkpoint *tapas-base* pada dataset Sequential Question Answering (SQA) | [Niels Rogge](https://github.com/nielsrogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb)|
|[Mengevaluasi TAPAS pada Table Fact Checking (TabFact)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb) | Cara mengevaluasi *TapasForSequenceClassification* yang telah dihaluskan dengan checkpoint *tapas-base-finetuned-tabfact* menggunakan kombinasi pustaka 🤗 datasets dan 🤗 transformers | [Niels Rogge](https://github.com/nielsrogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb)|
|[Penyesuaian halus mBART untuk penerjemahan](https://colab.research.google.com/github/vasudevgupta7/huggingface-tutorials/blob/main/translation_training.ipynb) | Cara melakukan fine-tuning mBART menggunakan Seq2SeqTrainer untuk terjemahan Hindi ke Inggris | [Vasudev Gupta](https://github.com/vasudevgupta7) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vasudevgupta7/huggingface-tutorials/blob/main/translation_training.ipynb)|
|[Lakukan fine-tuning LayoutLM pada FUNSD (dataset pemahaman formulir)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb) | Cara melakukan fine-tuning *LayoutLMForTokenClassification* pada dataset FUNSD untuk ekstraksi informasi dari dokumen hasil pindaian | [Niels Rogge](https://github.com/nielsrogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb)|
|[Menyesuaikan DistilGPT2 dan Menghasilkan Teks](https://colab.research.google.com/github/tripathiaakash/DistilGPT2-Tutorial/blob/main/distilgpt2_fine_tuning.ipynb) | Cara melakukan fine-tuning DistilGPT2 dan menghasilkan teks | [Aakash Tripathi](https://github.com/tripathiaakash) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tripathiaakash/DistilGPT2-Tutorial/blob/main/distilgpt2_fine_tuning.ipynb)|
|[Haluskan LED hingga 8K token](https://github.com/patrickvonplaten/notebooks/blob/master/Fine_tune_Longformer_Encoder_Decoder_(LED)_for_Summarization_on_pubmed.ipynb) | Cara melakukan fine-tuning LED pada pubmed untuk ringkasan jarak jauh | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tune_Longformer_Encoder_Decoder_(LED)_for_Summarization_on_pubmed.ipynb)|
|[Evaluasi LED di Arxiv](https://github.com/patrickvonplaten/notebooks/blob/master/LED_on_Arxiv.ipynb) | Cara mengevaluasi LED secara efektif pada ringkasan jarak jauh | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/LED_on_Arxiv.ipynb)|
|[Lakukan fine-tuning LayoutLM pada RVL-CDIP (dataset klasifikasi gambar dokumen)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForSequenceClassification_on_RVL_CDIP.ipynb) | Cara melakukan fine-tuning *LayoutLMForSequenceClassification* pada dataset RVL-CDIP untuk klasifikasi dokumen hasil pindaian | [Niels Rogge](https://github.com/nielsrogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForSequenceClassification_on_RVL_CDIP.ipynb)|
|[Decoding Wav2Vec2 CTC dengan penyesuaian GPT2](https://github.com/voidful/huggingface_notebook/blob/main/xlsr_gpt.ipynb) | Cara mendekode urutan CTC dengan penyesuaian model bahasa | [Eric Lam](https://github.com/voidful) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1e_z5jQHYbO2YKEaUgzb1ww1WwiAyydAj?usp=sharing)|
|[Menyesuaikan BART untuk ringkasan dalam dua bahasa dengan kelas Trainer](https://github.com/elsanns/xai-nlp-notebooks/blob/master/fine_tune_bart_summarization_two_langs.ipynb) | Cara melakukan fine-tuning BART untuk ringkasan dalam dua bahasa dengan kelas Trainer | [Eliza Szczechla](https://github.com/elsanns) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/elsanns/xai-nlp-notebooks/blob/master/fine_tune_bart_summarization_two_langs.ipynb)|
|[Mengevaluasi Big Bird pada Trivia QA](https://github.com/patrickvonplaten/notebooks/blob/master/Evaluating_Big_Bird_on_TriviaQA.ipynb) | Cara mengevaluasi BigBird pada penjawab pertanyaan dokumen panjang di Trivia QA | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Evaluating_Big_Bird_on_TriviaQA.ipynb)|
| [Membuat keterangan video menggunakan Wav2Vec2](https://github.com/Muennighoff/ytclipcc/blob/main/wav2vec_youtube_captions.ipynb) | Cara membuat caption YouTube dari video apa pun dengan mentranskripsi audio menggunakan Wav2Vec | [Niklas Muennighoff](https://github.com/Muennighoff) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Muennighoff/ytclipcc/blob/main/wav2vec_youtube_captions.ipynb) |
| [Menyesuaikan ulang Vision Transformer pada CIFAR-10 menggunakan PyTorch Lightning](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_PyTorch_Lightning.ipynb) | Cara melakukan fine-tuning Vision Transformer (ViT) pada CIFAR-10 menggunakan HuggingFace Transformers, Datasets, dan PyTorch Lightning | [Niels Rogge](https://github.com/nielsrogge) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_PyTorch_Lightning.ipynb) |
| [Menyesuaikan Vision Transformer pada CIFAR-10 menggunakan 🤗 Trainer](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_the_%F0%9F%A4%97_Trainer.ipynb) | Cara melakukan fine-tuning Vision Transformer (ViT) pada CIFAR-10 menggunakan HuggingFace Transformers, Datasets, dan 🤗 Trainer | [Niels Rogge](https://github.com/nielsrogge) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_the_%F0%9F%A4%97_Trainer.ipynb) |
| [Evaluasi LUKE pada Open Entity, dataset penipean entitas](https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_open_entity.ipynb) | Cara mengevaluasi *LukeForEntityClassification* pada dataset Open Entity | [Ikuya Yamada](https://github.com/ikuyamada) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_open_entity.ipynb) |
| [Evaluasi LUKE pada TACRED, dataset ekstraksi relasi](https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_tacred.ipynb) | Cara mengevaluasi *LukeForEntityPairClassification* pada dataset TACRED | [Ikuya Yamada](https://github.com/ikuyamada) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_tacred.ipynb) |
| [Evaluasi LUKE pada CoNLL-2003, sebuah benchmark NER yang penting](https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_conll_2003.ipynb) | Cara mengevaluasi *LukeForEntitySpanClassification* pada dataset CoNLL-2003 | [Ikuya Yamada](https://github.com/ikuyamada) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_conll_2003.ipynb) |
| [Evaluasi BigBird-Pegasus pada dataset PubMed](https://github.com/vasudevgupta7/bigbird/blob/main/notebooks/bigbird_pegasus_evaluation.ipynb) | Cara mengevaluasi *BigBirdPegasusForConditionalGeneration* pada dataset PubMed | [Vasudev Gupta](https://github.com/vasudevgupta7) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vasudevgupta7/bigbird/blob/main/notebooks/bigbird_pegasus_evaluation.ipynb) |
| [Klasifikasi Emosi Bicara dengan Wav2Vec2](https://github.com/m3hrdadfi/soxan/blob/main/notebooks/Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb) | Cara memanfaatkan model Wav2Vec2 yang telah dilatih sebelumnya untuk Klasifikasi Emosi pada dataset MEGA | [Mehrdad Farahani](https://github.com/m3hrdadfi) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/m3hrdadfi/soxan/blob/main/notebooks/Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb) |
| [Deteksi objek dalam gambar dengan DETR](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/DETR/DETR_minimal_example_(with_DetrFeatureExtractor).ipynb) | Cara menggunakan model *DetrForObjectDetection* yang telah dilatih untuk mendeteksi objek dalam gambar dan memvisualisasikan perhatian | [Niels Rogge](https://github.com/NielsRogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/DETR/DETR_minimal_example_(with_DetrFeatureExtractor).ipynb) |
| [Menyesuaikan DETR pada dataset deteksi objek kustom](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/DETR/Fine_tuning_DetrForObjectDetection_on_custom_dataset_(balloon).ipynb) | Cara melakukan fine-tuning *DetrForObjectDetection* pada dataset deteksi objek kustom | [Niels Rogge](https://github.com/NielsRogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/DETR/Fine_tuning_DetrForObjectDetection_on_custom_dataset_(balloon).ipynb) |
| [Menyesuaikan T5 untuk Pengenalan Entitas Bernama](https://github.com/ToluClassics/Notebooks/blob/main/T5_Ner_Finetuning.ipynb) | Cara melakukan fine-tuning *T5* pada Tugas Named Entity Recognition | [Ogundepo Odunayo](https://github.com/ToluClassics) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1obr78FY_cBmWY5ODViCmzdY6O1KB65Vc?usp=sharing) |
| [Fine-Tuning LLM Open-Source menggunakan QLoRA dengan MLflow dan PEFT](https://github.com/mlflow/mlflow/blob/master/docs/source/llms/transformers/tutorials/fine-tuning/transformers-peft.ipynb) | Cara menggunakan [QLoRA](https://github.com/artidoro/qlora) dan [PEFT](https://huggingface.co/docs/peft/en/index) untuk fine-tuning LLM dengan cara yang hemat memori, sambil menggunakan [MLflow](https://mlflow.org/docs/latest/llms/transformers/index.html) untuk mengelola pelacakan eksperimen | [Yuki Watanabe](https://github.com/B-Step62) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mlflow/mlflow/blob/master/docs/source/llms/transformers/tutorials/fine-tuning/transformers-peft.ipynb) |