Instructions to use zeromodels/all-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/all-MiniLM-L6-v2 with ZeroModels:
# pip install -U zeromodels # ZeroModels is pure Keras 3, so pick a backend: "jax", "torch" or "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" from zeromodels import AutoZModel # AutoZModel reads the repo's model_type and loads the matching class. # For a task head use the matching loader, e.g. AutoZMImageClassify / AutoZMDetect / # AutoZMSemanticSegment / AutoZMTextGenerate (see zeromodels.auto). model = AutoZModel.from_weights("zeromodels/all-MiniLM-L6-v2") - Keras
How to use zeromodels/all-MiniLM-L6-v2 with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://zeromodels/all-MiniLM-L6-v2") - sentence-transformers
How to use zeromodels/all-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("zeromodels/all-MiniLM-L6-v2") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 721 Bytes
267f574 e05486e 267f574 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"library_name": "zeromodels",
"preprocessor_module": "zeromodels.sentence_transformers",
"preprocessor_class": "ZMSentenceTransformerTokenizer",
"variant": "all-MiniLM-L6-v2",
"tokenizer_file": "tokenizer.json",
"backbone_type": "bert",
"tokenizer_config": {
"do_lower_case": true,
"unk_token": "[UNK]",
"sep_token": "[SEP]",
"pad_token": "[PAD]",
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"tokenize_chinese_chars": true,
"strip_accents": null,
"do_basic_tokenize": true,
"never_split": null,
"tokenizer_class": "BertTokenizer",
"model_max_length": 512
},
"sentence_transformer_config": {
"max_seq_length": 256,
"do_lower_case": false
}
}
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