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intfloat-multilingual-e5-base-english-fp16
abdulrahman-nuzha/intfloat-multilingual-e5-base-english-fp16
intfloat-multilingual-e5-base-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
- c9823256
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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-base-english-fp16
This model is a fine-tuned version of intfloat/multilingual-e5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3280
- Accuracy: 0.8806
- Precision: 0.8826
- Recall: 0.8806
- F1: 0.8787
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 |
|---|---|---|---|---|---|---|---|
| 1.1088 | 0.3922 | 50 | 1.0093 | 0.5010 | 0.6039 | 0.5010 | 0.3601 |
| 0.9081 | 0.7843 | 100 | 0.7840 | 0.6262 | 0.6965 | 0.6262 | 0.5510 |
| 0.6279 | 1.1725 | 150 | 0.4643 | 0.8350 | 0.8338 | 0.8350 | 0.8330 |
| 0.41 | 1.5647 | 200 | 0.3708 | 0.8610 | 0.8615 | 0.8610 | 0.8588 |
| 0.3623 | 1.9569 | 250 | 0.3420 | 0.8679 | 0.8717 | 0.8679 | 0.8650 |
| 0.2924 | 2.3451 | 300 | 0.3280 | 0.8806 | 0.8826 | 0.8806 | 0.8787 |
| 0.2822 | 2.7373 | 350 | 0.3052 | 0.8861 | 0.8867 | 0.8861 | 0.8849 |
| 0.2532 | 3.1255 | 400 | 0.3366 | 0.8861 | 0.8880 | 0.8861 | 0.8845 |
| 0.2101 | 3.5176 | 450 | 0.2986 | 0.8910 | 0.8904 | 0.8910 | 0.8903 |
| 0.2267 | 3.9098 | 500 | 0.3331 | 0.8811 | 0.8821 | 0.8811 | 0.8794 |
| 0.1641 | 4.2980 | 550 | 0.3474 | 0.8895 | 0.8893 | 0.8895 | 0.8884 |
| 0.1653 | 4.6902 | 600 | 0.3467 | 0.8910 | 0.8912 | 0.8910 | 0.8901 |
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