Instructions to use BM-K/KoMiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BM-K/KoMiniLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BM-K/KoMiniLM")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BM-K/KoMiniLM") model = AutoModel.from_pretrained("BM-K/KoMiniLM") - Notebooks
- Google Colab
- Kaggle
add model
Browse files- config.json +2 -4
- pytorch_model.bin +2 -2
config.json
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"id2label": {
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"0": "
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"1": "1"
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"label2id": {
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"id2label": {
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"label2id": {
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"LABEL_0": 0
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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oid sha256:fb8e1666d2fefb6c3fe8741f68aa942f803dbfbb0ee19a10a0aec198dbce8cb7
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size 93171753
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