Instructions to use exaggerated/text2vec-base-chinese-rs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use exaggerated/text2vec-base-chinese-rs with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("exaggerated/text2vec-base-chinese-rs") model = AutoModel.from_pretrained("exaggerated/text2vec-base-chinese-rs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download modules.json from exaggerated/text2vec-base-chinese-rs: direct link, hf CLI and curl.
- Browser
- Download file 230 Bytes
-
https://huggingface.co/exaggerated/text2vec-base-chinese-rs/resolve/refs%2Fpr%2F3/modules.json
- Command line
-
hf download hf://exaggerated/text2vec-base-chinese-rs@refs/pr/3/modules.json
-
curl -L -o modules.json https://huggingface.co/exaggerated/text2vec-base-chinese-rs/resolve/refs%2Fpr%2F3/modules.json
230 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] | |