Instructions to use jxm/cde-small-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jxm/cde-small-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jxm/cde-small-v3", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jxm/cde-small-v3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architecture": "transductive", | |
| "architectures": [ | |
| "ContextualDocumentEmbeddingTransformer" | |
| ], | |
| "attn_implementation": null, | |
| "auto_map": { | |
| "AutoConfig": "misc.ContextualModelConfig", | |
| "AutoModel": "model.ContextualDocumentEmbeddingTransformer" | |
| }, | |
| "autoregressive_backbone": false, | |
| "cache_dir": null, | |
| "config_name": null, | |
| "dataset_backbone": null, | |
| "disable_dropout": true, | |
| "disable_transductive_rotary_embedding": true, | |
| "embedder": "chandar-lab/NeoBERT", | |
| "embedder_rerank": "sentence-transformers/gtr-t5-base", | |
| "embedding_output_dim": null, | |
| "limit_layers": null, | |
| "limit_layers_first_stage": null, | |
| "logit_scale": 50.0, | |
| "max_seq_length": 512, | |
| "model_revision": "main", | |
| "pool_ignore_contextual_tokens": true, | |
| "pool_ignore_instruction_tokens": true, | |
| "pooling_strategy": "mean", | |
| "tokenizer_name": null, | |
| "torch_dtype": "float32", | |
| "transductive_corpus_size": 512, | |
| "transductive_sequence_dropout_prob": 0.0, | |
| "transductive_tie_token_embeddings": false, | |
| "transductive_tokens_per_document": 1, | |
| "transformers_version": "4.51.0" | |
| } | |