Instructions to use radames/all-mpnet-base-v2-feature-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radames/all-mpnet-base-v2-feature-extraction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="radames/all-mpnet-base-v2-feature-extraction")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("radames/all-mpnet-base-v2-feature-extraction") model = AutoModelForMaskedLM.from_pretrained("radames/all-mpnet-base-v2-feature-extraction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download modules.json from radames/all-mpnet-base-v2-feature-extraction: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/radames/all-mpnet-base-v2-feature-extraction/resolve/main/modules.json
- Command line
-
hf download hf://radames/all-mpnet-base-v2-feature-extraction/modules.json
-
curl -L -o modules.json https://huggingface.co/radames/all-mpnet-base-v2-feature-extraction/resolve/main/modules.json
349 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |