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intfloat-multilingual-e5-large-english-fp16
abdulrahman-nuzha/intfloat-multilingual-e5-large-english-fp16
intfloat-multilingual-e5-large-english-fp16 is a text classification model from abdulrahman-nuzha. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Apr 16, 2025
- Updated
- Apr 16, 2025
- SHA
- c4ef1777
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
intfloat-multilingual-e5-large-english-fp16
This model is a fine-tuned version of intfloat/multilingual-e5-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3003
- Accuracy: 0.8851
- Precision: 0.8857
- Recall: 0.8851
- F1: 0.8835
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: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 0.3
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 1.0422 | 0.3922 | 50 | 0.8866 | 0.6081 | 0.6817 | 0.6081 | 0.5308 |
| 0.703 | 0.7843 | 100 | 0.4687 | 0.8266 | 0.8265 | 0.8266 | 0.8242 |
| 0.3701 | 1.1725 | 150 | 0.3189 | 0.8787 | 0.8783 | 0.8787 | 0.8784 |
| 0.3018 | 1.5647 | 200 | 0.3163 | 0.8861 | 0.8855 | 0.8861 | 0.8852 |
| 0.2988 | 1.9569 | 250 | 0.3223 | 0.8797 | 0.8828 | 0.8797 | 0.8770 |
| 0.2324 | 2.3451 | 300 | 0.3003 | 0.8851 | 0.8857 | 0.8851 | 0.8835 |
| 0.2337 | 2.7373 | 350 | 0.2901 | 0.8905 | 0.8909 | 0.8905 | 0.8904 |
| 0.2066 | 3.1255 | 400 | 0.3938 | 0.8851 | 0.8866 | 0.8851 | 0.8831 |
| 0.1711 | 3.5176 | 450 | 0.3373 | 0.8905 | 0.8910 | 0.8905 | 0.8907 |
| 0.1668 | 3.9098 | 500 | 0.3527 | 0.8861 | 0.8859 | 0.8861 | 0.8848 |
Framework versions
- Transformers 4.51.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Apr 16, 2025
- Updated
- Apr 16, 2025