Downloads · 30 days
10
19% of all-time downloads
intfloat-multilingual-e5-base-arabic-fp16-allagree
abdulrahman-nuzha/intfloat-multilingual-e5-base-arabic-fp16-allagree
intfloat-multilingual-e5-base-arabic-fp16-allagree 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. --
All-time downloads
53
Public
Parameters
278M
1.1 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors1.1 GB · 98%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Apr 15, 2025
- Updated
- Apr 15, 2025
- SHA
- f77a81f4
Try a prompt
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-arabic-fp16-allagree
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: 0.2194
- Accuracy: 0.9207
- Precision: 0.9221
- Recall: 0.9207
- F1: 0.9211
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 |
|---|---|---|---|---|---|---|---|
| 1.0293 | 0.7463 | 50 | 0.8440 | 0.7472 | 0.8005 | 0.7472 | 0.6652 |
| 0.599 | 1.4925 | 100 | 0.3097 | 0.9021 | 0.9014 | 0.9021 | 0.9017 |
| 0.2581 | 2.2388 | 150 | 0.2348 | 0.9198 | 0.9207 | 0.9198 | 0.9201 |
| 0.2087 | 2.9851 | 200 | 0.2194 | 0.9207 | 0.9221 | 0.9207 | 0.9211 |
| 0.1552 | 3.7313 | 250 | 0.2203 | 0.9300 | 0.9301 | 0.9300 | 0.9299 |
| 0.0989 | 4.4776 | 300 | 0.2785 | 0.9179 | 0.9181 | 0.9179 | 0.9168 |
| 0.088 | 5.2239 | 350 | 0.2658 | 0.9263 | 0.9271 | 0.9263 | 0.9266 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Apr 15, 2025
- Updated
- Apr 15, 2025