Feature Extraction
Transformers
Safetensors
bert
tevatron
tevatron-elastic
information-retrieval
reranker
elastic
text-embeddings-inference
Instructions to use utahnlp/tevatron-elastic-bert-reranker-depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use utahnlp/tevatron-elastic-bert-reranker-depth with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="utahnlp/tevatron-elastic-bert-reranker-depth", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("utahnlp/tevatron-elastic-bert-reranker-depth") model = AutoModel.from_pretrained("utahnlp/tevatron-elastic-bert-reranker-depth", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 350 Bytes
643e670 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"backend": "tokenizers",
"cls_token": "[CLS]",
"do_lower_case": true,
"is_local": true,
"local_files_only": false,
"mask_token": "[MASK]",
"model_max_length": 512,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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