Instructions to use subhasisj/de-TAPT-MLM-MiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subhasisj/de-TAPT-MLM-MiniLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subhasisj/de-TAPT-MLM-MiniLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subhasisj/de-TAPT-MLM-MiniLM") model = AutoModelForMaskedLM.from_pretrained("subhasisj/de-TAPT-MLM-MiniLM") - Notebooks
- Google Colab
- Kaggle
Training Completed
Browse files
pytorch_model.bin
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runs/May12_19-28-56_614d8bd46082/events.out.tfevents.1652383775.614d8bd46082.70.0
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