Feature Extraction
Transformers
Safetensors
bert
tevatron
tevatron-elastic
information-retrieval
retriever
elastic
text-embeddings-inference
Instructions to use utahnlp/tevatron-elastic-bert-retriever-depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use utahnlp/tevatron-elastic-bert-retriever-depth with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="utahnlp/tevatron-elastic-bert-retriever-depth")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("utahnlp/tevatron-elastic-bert-retriever-depth") model = AutoModel.from_pretrained("utahnlp/tevatron-elastic-bert-retriever-depth") - Notebooks
- Google Colab
- Kaggle
Upload Tevatron-Elastic checkpoint (final model)
Browse files- README.md +29 -0
- config.json +30 -0
- granularities.json +36 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +15 -0
README.md
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---
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license: apache-2.0
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base_model: google-bert/bert-base-uncased
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library_name: transformers
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tags:
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- tevatron
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- tevatron-elastic
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- information-retrieval
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- retriever
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- elastic
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---
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# tevatron-elastic-bert-retriever-depth
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A **retriever** trained with [Tevatron-Elastic](https://github.com/zhichaoxu-shufe/tevatron-elastic),
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which trains one checkpoint to serve many operating points along the depth / width / token
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compression axes. This checkpoint is an elastic **depth** axis (early exit): one checkpoint serves several layer counts.
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- **Base model:** `google-bert/bert-base-uncased`
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- **Task:** retriever
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- **Elastic axis:** depth
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- **Training data:** [rlhn/rlhn-680K](https://huggingface.co/datasets/rlhn/rlhn-680K), max length 512, `query:`/`passage:` prefixes.
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Full-point BEIR-15 nDCG@10: **0.453**.
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Load with the Tevatron-Elastic framework and select an operating point with `prune_to` /
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`encode_at`; see the repository for usage. Part of a release of 20 checkpoints (3 backbones,
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retrieval and reranking, all compression axes) accompanying the Tevatron-Elastic paper. Reported as
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a reproducibility resource, not a state-of-the-art claim.
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config.json
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{
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"add_cross_attention": false,
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": null,
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"classifier_dropout": null,
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"dtype": "bfloat16",
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"eos_token_id": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"is_decoder": false,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"tie_word_embeddings": true,
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"transformers_version": "5.12.1",
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"type_vocab_size": 2,
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"use_cache": false,
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"vocab_size": 30522
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}
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granularities.json
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{
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"pooling": "cls",
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"normalize": true,
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"points": [
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{
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"layer": 2,
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"dim": 768,
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"name": null
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},
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{
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"layer": 4,
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"dim": 768,
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"name": null
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},
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{
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"layer": 6,
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"dim": 768,
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"name": null
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},
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{
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"layer": 8,
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"dim": 768,
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"name": null
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},
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{
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"layer": 10,
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"dim": 768,
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"name": null
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},
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{
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"layer": 12,
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"dim": 768,
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"name": null
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}
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]
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1721649bfc11530ed1e09b35dc3c45310ec98b182a28499e29f134cc3c654d8c
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size 218986904
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"is_local": true,
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"local_files_only": false,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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