Feature Extraction
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use comet24082002/finetuned_bge_ver13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use comet24082002/finetuned_bge_ver13 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="comet24082002/finetuned_bge_ver13")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("comet24082002/finetuned_bge_ver13") model = AutoModel.from_pretrained("comet24082002/finetuned_bge_ver13", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download modules.json from comet24082002/finetuned_bge_ver13: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/comet24082002/finetuned_bge_ver13/resolve/main/modules.json
- Command line
-
hf download hf://comet24082002/finetuned_bge_ver13/modules.json
-
curl -L -o modules.json https://huggingface.co/comet24082002/finetuned_bge_ver13/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" | |
| } | |
| ] |