Instructions to use adriansanz/rerank_v7_5_ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adriansanz/rerank_v7_5_ep with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="adriansanz/rerank_v7_5_ep")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("adriansanz/rerank_v7_5_ep") model = AutoModelForSequenceClassification.from_pretrained("adriansanz/rerank_v7_5_ep", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from adriansanz/rerank_v7_5_ep: direct link, hf CLI and curl.
- Browser
- Download file 808 Bytes
-
https://huggingface.co/adriansanz/rerank_v7_5_ep/resolve/main/config.json
- Command line
-
hf download hf://adriansanz/rerank_v7_5_ep/config.json
-
curl -L -o config.json https://huggingface.co/adriansanz/rerank_v7_5_ep/resolve/main/config.json
808 Bytes
| { | |
| "_name_or_path": "BAAI/bge-reranker-v2-m3", | |
| "architectures": [ | |
| "XLMRobertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "LABEL_0" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "label2id": { | |
| "LABEL_0": 0 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 8194, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.44.2", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |