Downloads · 30 days
10
5% of all-time downloads
distilbert-base-multilingual-cased-WNUT-ner
dmargutierrez/distilbert-base-multilingual-cased-WNUT-ner
distilbert-base-multilingual-cased-WNUT-ner is a token classification model from dmargutierrez. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
213
Public
Repo size
5.9 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin539 MB · 99%
At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Mar 15, 2023
- Updated
- Mar 15, 2023
- SHA
- 0859fb0d
Try a prompt
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. -->
distilbert-base-multilingual-cased-WNUT-ner
This model is a fine-tuned version of distilbert-base-multilingual-cased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3516
- Precision: 0.5497
- Recall: 0.3642
- F1: 0.4381
- Accuracy: 0.9469
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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2727 | 0.6626 | 0.2530 | 0.3662 | 0.9402 |
| No log | 2.0 | 426 | 0.2636 | 0.5895 | 0.2715 | 0.3718 | 0.9429 |
| 0.1729 | 3.0 | 639 | 0.2933 | 0.5931 | 0.3040 | 0.4020 | 0.9447 |
| 0.1729 | 4.0 | 852 | 0.2861 | 0.5437 | 0.3457 | 0.4227 | 0.9453 |
| 0.0503 | 5.0 | 1065 | 0.3270 | 0.5627 | 0.3494 | 0.4311 | 0.9455 |
| 0.0503 | 6.0 | 1278 | 0.3277 | 0.5451 | 0.3531 | 0.4286 | 0.9463 |
| 0.0503 | 7.0 | 1491 | 0.3471 | 0.5828 | 0.3457 | 0.4340 | 0.9467 |
| 0.0231 | 8.0 | 1704 | 0.3594 | 0.5801 | 0.3457 | 0.4332 | 0.9464 |
| 0.0231 | 9.0 | 1917 | 0.3550 | 0.5567 | 0.3503 | 0.4300 | 0.9467 |
| 0.0121 | 10.0 | 2130 | 0.3516 | 0.5497 | 0.3642 | 0.4381 | 0.9469 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 15, 2023
- Updated
- Mar 15, 2023