Downloads · 30 days
7
14% of all-time downloads
xlm-roberta-base-MLTC-rob
NaeemCSECUET18/xlm-roberta-base-MLTC-rob
xlm-roberta-base-MLTC-rob is a text classification model from NaeemCSECUET18. 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
50
Public
Parameters
278M
73.4 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
- Jun 2, 2024
- Updated
- Jun 25, 2024
- SHA
- 5ffe1bdd
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. -->
xlm-roberta-base-MLTC-rob
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3645
- F1: 0.8629
- F1 Weighted: 0.8632
- Roc Auc: 0.8598
- Accuracy: 0.6067
- Hamming Loss: 0.1401
- Jaccard Score: 0.7588
- Zero One Loss: 0.3933
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | F1 Weighted | Roc Auc | Accuracy | Hamming Loss | Jaccard Score | Zero One Loss |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.5856 | 1.0 | 73 | 0.5884 | 0.7201 | 0.6619 | 0.6835 | 0.3393 | 0.3162 | 0.5627 | 0.6607 |
| 0.5053 | 2.0 | 146 | 0.4688 | 0.7718 | 0.7159 | 0.7712 | 0.4139 | 0.2288 | 0.6284 | 0.5861 |
| 0.3929 | 3.0 | 219 | 0.4002 | 0.8410 | 0.8413 | 0.8334 | 0.5347 | 0.1665 | 0.7256 | 0.4653 |
| 0.3293 | 4.0 | 292 | 0.3816 | 0.8471 | 0.8453 | 0.8399 | 0.5527 | 0.1600 | 0.7348 | 0.4473 |
| 0.3242 | 5.0 | 365 | 0.3607 | 0.8550 | 0.8538 | 0.8515 | 0.5784 | 0.1485 | 0.7467 | 0.4216 |
| 0.3228 | 6.0 | 438 | 0.3776 | 0.8495 | 0.8462 | 0.8437 | 0.5707 | 0.1562 | 0.7384 | 0.4293 |
| 0.2713 | 7.0 | 511 | 0.4086 | 0.8453 | 0.8412 | 0.8373 | 0.5630 | 0.1626 | 0.7320 | 0.4370 |
| 0.2519 | 8.0 | 584 | 0.3711 | 0.8534 | 0.8531 | 0.8489 | 0.5861 | 0.1510 | 0.7443 | 0.4139 |
| 0.2724 | 9.0 | 657 | 0.3645 | 0.8629 | 0.8632 | 0.8598 | 0.6067 | 0.1401 | 0.7588 | 0.3933 |
| 0.2484 | 10.0 | 730 | 0.3669 | 0.8586 | 0.8585 | 0.8553 | 0.5964 | 0.1446 | 0.7522 | 0.4036 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Jun 2, 2024
- Updated
- Jun 25, 2024