Feature Extraction
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use comet24082002/finetuned_bge_ver15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use comet24082002/finetuned_bge_ver15 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="comet24082002/finetuned_bge_ver15")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("comet24082002/finetuned_bge_ver15") model = AutoModel.from_pretrained("comet24082002/finetuned_bge_ver15", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from comet24082002/finetuned_bge_ver15: direct link, hf CLI and curl.
- Browser
- Download file 1.18 kB
-
https://huggingface.co/comet24082002/finetuned_bge_ver15/resolve/main/README.md
- Command line
-
hf download hf://comet24082002/finetuned_bge_ver15/README.md
-
curl -L -o README.md https://huggingface.co/comet24082002/finetuned_bge_ver15/resolve/main/README.md
1.18 kB
metadata
license: mit
base_model: BAAI/bge-m3
tags:
- generated_from_trainer
model-index:
- name: finetuned_bge_ver15
results: []
finetuned_bge_ver15
This model is a fine-tuned version of BAAI/bge-m3 on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2