Downloads · 30 days
4
9% of all-time downloads
20230928-2-xlm-roberta-base-new
soBeauty/20230928-2-xlm-roberta-base-new
20230928-2-xlm-roberta-base-new is a fill-mask model from soBeauty. Use it when you need the model to fill a missing word. 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
46
Public
Repo size
12.2 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin1.1 GB · 100%
At a glance
- Task
- Fill-Mask
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Sep 28, 2023
- Updated
- Sep 30, 2023
- SHA
- 7e5101ac
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. -->
20230928-2-xlm-roberta-base-new
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Accuracy: 0.4964
- Loss: 2.7563
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: 8
- eval_batch_size: 8
- 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 | Accuracy | Validation Loss |
|---|---|---|---|---|
| 4.4132 | 0.46 | 200 | 0.2795 | nan |
| 4.0135 | 0.91 | 400 | 0.3245 | nan |
| 3.874 | 1.37 | 600 | 0.2875 | nan |
| 3.6614 | 1.82 | 800 | 0.3380 | 3.4541 |
| 3.5348 | 2.28 | 1000 | 0.3618 | 3.2732 |
| 3.4756 | 2.73 | 1200 | 0.3986 | nan |
| 3.3677 | 3.19 | 1400 | 0.4245 | nan |
| 3.3707 | 3.64 | 1600 | 0.4044 | 3.3262 |
| 3.1909 | 4.1 | 1800 | 0.3968 | nan |
| 3.1404 | 4.56 | 2000 | 0.4360 | 3.2661 |
| 2.9553 | 5.01 | 2200 | 0.4752 | 2.7995 |
| 2.9725 | 5.47 | 2400 | 0.4255 | 2.9909 |
| 2.9121 | 5.92 | 2600 | 0.4724 | 2.7879 |
| 2.8641 | 6.38 | 2800 | 0.4727 | nan |
| 2.7376 | 6.83 | 3000 | 0.4414 | 2.9275 |
| 2.8078 | 7.29 | 3200 | 0.4766 | 2.5626 |
| 2.8166 | 7.74 | 3400 | 0.48 | nan |
| 2.6979 | 8.2 | 3600 | 0.5013 | nan |
| 2.7525 | 8.66 | 3800 | 0.4915 | 2.8394 |
| 2.6757 | 9.11 | 4000 | 0.5013 | nan |
| 2.6633 | 9.57 | 4200 | 0.4964 | 2.7563 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Fill-Mask
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Sep 28, 2023
- Updated
- Sep 30, 2023