Instructions to use subhasisj/Zh-Mulitlingual-MiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subhasisj/Zh-Mulitlingual-MiniLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subhasisj/Zh-Mulitlingual-MiniLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subhasisj/Zh-Mulitlingual-MiniLM") model = AutoModelForMaskedLM.from_pretrained("subhasisj/Zh-Mulitlingual-MiniLM") - Notebooks
- Google Colab
- Kaggle
Zh-Mulitlingual-MiniLM
This model is a fine-tuned version of microsoft/Multilingual-MiniLM-L12-H384 on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
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