Instructions to use xtlalert/ms-marco-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use xtlalert/ms-marco-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("xtlalert/ms-marco-onnx") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download config.json from xtlalert/ms-marco-onnx: direct link, hf CLI and curl.
- Browser
- Download file 823 Bytes
-
https://huggingface.co/xtlalert/ms-marco-onnx/resolve/main/config.json
- Command line
-
hf download hf://xtlalert/ms-marco-onnx/config.json
-
curl -L -o config.json https://huggingface.co/xtlalert/ms-marco-onnx/resolve/main/config.json
823 Bytes
| { | |
| "_name_or_path": "cross-encoder/ms-marco-MiniLM-L6-v2", | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 384, | |
| "id2label": { | |
| "0": "LABEL_0" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1536, | |
| "label2id": { | |
| "LABEL_0": 0 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 6, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "sbert_ce_default_activation_function": "torch.nn.modules.linear.Identity", | |
| "transformers_version": "4.48.3", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |