Sentence Similarity
sentence-transformers
Safetensors
GGUF
Indonesian
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:6198
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Pustekhan-ITB/indoedu-e5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Pustekhan-ITB/indoedu-e5-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Pustekhan-ITB/indoedu-e5-base") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - llama-cpp-python
How to use Pustekhan-ITB/indoedu-e5-base with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Pustekhan-ITB/indoedu-e5-base", filename="indoedu-e5-base.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Pustekhan-ITB/indoedu-e5-base with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Pustekhan-ITB/indoedu-e5-base # Run inference directly in the terminal: llama-cli -hf Pustekhan-ITB/indoedu-e5-base
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Pustekhan-ITB/indoedu-e5-base # Run inference directly in the terminal: llama-cli -hf Pustekhan-ITB/indoedu-e5-base
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Pustekhan-ITB/indoedu-e5-base # Run inference directly in the terminal: ./llama-cli -hf Pustekhan-ITB/indoedu-e5-base
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Pustekhan-ITB/indoedu-e5-base # Run inference directly in the terminal: ./build/bin/llama-cli -hf Pustekhan-ITB/indoedu-e5-base
Use Docker
docker model run hf.co/Pustekhan-ITB/indoedu-e5-base
- LM Studio
- Jan
- Ollama
How to use Pustekhan-ITB/indoedu-e5-base with Ollama:
ollama run hf.co/Pustekhan-ITB/indoedu-e5-base
- Unsloth Studio new
How to use Pustekhan-ITB/indoedu-e5-base with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Pustekhan-ITB/indoedu-e5-base to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Pustekhan-ITB/indoedu-e5-base to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Pustekhan-ITB/indoedu-e5-base to start chatting
- Docker Model Runner
How to use Pustekhan-ITB/indoedu-e5-base with Docker Model Runner:
docker model run hf.co/Pustekhan-ITB/indoedu-e5-base
- Lemonade
How to use Pustekhan-ITB/indoedu-e5-base with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Pustekhan-ITB/indoedu-e5-base
Run and chat with the model
lemonade run user.indoedu-e5-base-{{QUANT_TAG}}List all available models
lemonade list
| { | |
| "_name_or_path": "intfloat/multilingual-e5-base", | |
| "architectures": [ | |
| "XLMRobertaModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.48.3", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |