Feature Extraction
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use comet24082002/finetuned_bge_ver6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use comet24082002/finetuned_bge_ver6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="comet24082002/finetuned_bge_ver6")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("comet24082002/finetuned_bge_ver6") model = AutoModel.from_pretrained("comet24082002/finetuned_bge_ver6") - Notebooks
- Google Colab
- Kaggle
finetuned_bge_ver6
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: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7.0
- 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
- Downloads last month
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Model tree for comet24082002/finetuned_bge_ver6
Base model
BAAI/bge-m3