Instructions to use vocab-transformers/cross_encoder-msmarco-distilbert-word2vec256k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vocab-transformers/cross_encoder-msmarco-distilbert-word2vec256k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vocab-transformers/cross_encoder-msmarco-distilbert-word2vec256k")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vocab-transformers/cross_encoder-msmarco-distilbert-word2vec256k") model = AutoModelForSequenceClassification.from_pretrained("vocab-transformers/cross_encoder-msmarco-distilbert-word2vec256k") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0da60e987af136e6410b1b45de76e5d5c90731d8901feccbb0e7339e017adf3c
- Size of remote file:
- 961 MB
- SHA256:
- 7eabe1045290bb03411ddfd73fb87a43f997a64c3bffcefd9939824eebe6b7c1
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