Instructions to use ateneaxvalac/checkpoints_e5_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ateneaxvalac/checkpoints_e5_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ateneaxvalac/checkpoints_e5_small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ateneaxvalac/checkpoints_e5_small") model = AutoModelForSequenceClassification.from_pretrained("ateneaxvalac/checkpoints_e5_small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
checkpoints_e5_small
This model is a fine-tuned version of intfloat/multilingual-e5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5258
- Accuracy: 0.7690
- F1: 0.7981
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: 2e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.4446 | 1.0 | 3678 | 0.5094 | 0.7580 | 0.7911 |
| 0.3889 | 2.0 | 7356 | 0.5087 | 0.7691 | 0.7993 |
| 0.3686 | 3.0 | 11034 | 0.5258 | 0.7690 | 0.7981 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.11.0+cu130
- Datasets 5.0.1
- Tokenizers 0.22.2
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Model tree for ateneaxvalac/checkpoints_e5_small
Base model
intfloat/multilingual-e5-small