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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`
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by tomaarsen HF Staff - opened
README.md
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#cross_encoder-msmarco-word2vec256k
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This CrossEncoder was trained with MarginMSE loss from the [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://hf.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) checkpoint. **Word embedding matrix has been frozen during training**.
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---
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library_name: sentence-transformers
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pipeline_tag: text-ranking
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---
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#cross_encoder-msmarco-word2vec256k
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This CrossEncoder was trained with MarginMSE loss from the [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://hf.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) checkpoint. **Word embedding matrix has been frozen during training**.
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