Downloads · 30 days
4
12% of all-time downloads
XLM-RoBERTa-CERED1
stulcrad/XLM-RoBERTa-CERED1
XLM-RoBERTa-CERED1 is a machine learning model from stulcrad. Use it for the machine learning task on the model card, and read the license before you ship it in a product. 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
34
Public
Parameters
560M
9 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors2.2 GB · 99%
At a glance
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Mar 6, 2025
- Updated
- Jun 27, 2025
- SHA
- e08b308f
Base models
Datasets
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. -->
XLM-RoBERTa-CERED1
This model is a fine-tuned version of xlm-roberta-large on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 0.4276
- Accuracy: 0.9007
- Micro Precision: 0.9007
- Micro Recall: 0.9007
- Micro F1: 0.9007
- Macro Precision: 0.8729
- Macro Recall: 0.8466
- Macro F1: 0.8530
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use 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_steps: 2000
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro Precision | Micro Recall | Micro F1 | Macro Precision | Macro Recall | Macro F1 |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.3873 | 1.0 | 30125 | 0.4037 | 0.8800 | 0.8800 | 0.8800 | 0.8800 | 0.8383 | 0.8147 | 0.8171 |
| 0.2926 | 2.0 | 60250 | 0.3772 | 0.8934 | 0.8934 | 0.8934 | 0.8934 | 0.8609 | 0.8383 | 0.8429 |
| 0.2148 | 3.0 | 90375 | 0.3846 | 0.8990 | 0.8990 | 0.8990 | 0.8990 | 0.8665 | 0.8503 | 0.8527 |
| 0.1532 | 4.0 | 120500 | 0.4068 | 0.9046 | 0.9046 | 0.9046 | 0.9046 | 0.8722 | 0.8588 | 0.8610 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Mar 6, 2025
- Updated
- Jun 27, 2025