Feature Extraction
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use comet24082002/finetuned_bge_ver25 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use comet24082002/finetuned_bge_ver25 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="comet24082002/finetuned_bge_ver25")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("comet24082002/finetuned_bge_ver25") model = AutoModel.from_pretrained("comet24082002/finetuned_bge_ver25", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from comet24082002/finetuned_bge_ver25: direct link, hf CLI and curl.
- Browser
- Download file 6.71 kB
-
https://huggingface.co/comet24082002/finetuned_bge_ver25/resolve/main/training_args.bin
- Command line
-
hf download hf://comet24082002/finetuned_bge_ver25/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/comet24082002/finetuned_bge_ver25/resolve/main/training_args.bin
6.71 kB
- Xet hash:
- 8d90d1a922d9563f1dcbbd6c80c329ee4aa9945d3a1c221475b53a5a418601be
- Size of remote file:
- 6.71 kB
- SHA256:
- e490142c7f94f6e78d3ec95cd91d7ef85c1022bc6940ef7a675fec11b2cfa7a0
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