From the model card
Fields below are copied from the tags and counters on the HuggingFace repository LazarusNLP/all-indo-e5-small-v4 at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- LazarusNLP
- Pipeline tag
- sentence-similarity
- Library
- Sentence Transformers, Transformers
- Weight formats
- ONNX, safetensors
- Datasets declared
- indonli, indolem/indo_story_cloze, unicamp-dl/mmarco, miracl/miracl, nthakur/swim-ir-monolingual, LazarusNLP/multilingual-NLI-26lang-2mil7-id, SEACrowd/wrete, SEACrowd/indolem_ntp and 5 more on the model card
- Downloads (HF counter at last fetch)
- 357,255
- Likes (HF counter at last fetch)
- 13
- Model card
- https://huggingface.co/LazarusNLP/all-indo-e5-small-v4
Use cases
- Semantic search over Indonesian-language document collections
- Indonesian-language FAQ retrieval for chatbot grounding
- Clustering Indonesian news articles by topic
- Embedding Indonesian social media text for similarity tasks
Pros
- Specific fine-tuning on Indonesian text outperforms generic multilingual embedders on Bahasa
- Small model size keeps inference cost low
- Fills a real gap — Indonesian NLP resources are sparser than major languages
- Iterative v4 release suggests ongoing quality improvements
Cons
- Primarily Bahasa Indonesia standard — Javanese, Sundanese dialects not covered
- No published MTEB Indonesian subset scores to compare against alternatives
- Small model limits embedding quality on long passages
- Community project without commercial support
Tags
sentence-transformersonnxsafetensorsbertfeature-extractionsentence-similaritytransformersdataset:indonlidataset:indolem/indo_story_clozedataset:unicamp-dl/mmarcodataset:miracl/miracldataset:nthakur/swim-ir-monolingualdataset:LazarusNLP/multilingual-NLI-26lang-2mil7-iddataset:SEACrowd/wretedataset:SEACrowd/indolem_ntpdataset:khalidalt/tydiqa-goldpdataset:SEACrowd/facqadataset:indonesian-nlp/lfqa_iddataset:jakartaresearch/indoqadataset:jakartaresearch/id-paraphrase-detection