Instructions to use vocab-transformers/msmarco-distilbert-word2vec256k-MLM_230k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vocab-transformers/msmarco-distilbert-word2vec256k-MLM_230k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vocab-transformers/msmarco-distilbert-word2vec256k-MLM_230k")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vocab-transformers/msmarco-distilbert-word2vec256k-MLM_230k") model = AutoModelForMaskedLM.from_pretrained("vocab-transformers/msmarco-distilbert-word2vec256k-MLM_230k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Model
This model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 230k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs. The word embedding matrix was frozen.
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