Downloads · 30 days
3
13% of all-time downloads
BERT-multilingual-finetuned-CEFR_ner-3000news
DioBot2000/BERT-multilingual-finetuned-CEFR_ner-3000news
BERT-multilingual-finetuned-CEFR_ner-3000news is a token classification model from DioBot2000. 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
24
Public
Parameters
177M
2.8 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors709 MB · 99%
At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- May 24, 2024
- Updated
- May 24, 2024
- SHA
- ee5dc86c
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. -->
BERT-multilingual-finetuned-CEFR_ner-3000news
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5691
- Accuracy: 0.4044
- Precision: 0.4949
- Recall: 0.6593
- F1: 0.4688
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: 12
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 132 | 0.5657 | 0.3739 | 0.5044 | 0.5333 | 0.4050 |
| No log | 2.0 | 264 | 0.5076 | 0.3859 | 0.5011 | 0.5712 | 0.4225 |
| No log | 3.0 | 396 | 0.4845 | 0.3925 | 0.4690 | 0.6167 | 0.4351 |
| 0.4009 | 4.0 | 528 | 0.4981 | 0.3985 | 0.4956 | 0.6180 | 0.4514 |
| 0.4009 | 5.0 | 660 | 0.5136 | 0.3976 | 0.4913 | 0.6348 | 0.4570 |
| 0.4009 | 6.0 | 792 | 0.5092 | 0.4019 | 0.5004 | 0.6434 | 0.4655 |
| 0.4009 | 7.0 | 924 | 0.5235 | 0.4012 | 0.4837 | 0.6434 | 0.4555 |
| 0.1848 | 8.0 | 1056 | 0.5327 | 0.4033 | 0.4948 | 0.6519 | 0.4662 |
| 0.1848 | 9.0 | 1188 | 0.5640 | 0.4033 | 0.4920 | 0.6536 | 0.4638 |
| 0.1848 | 10.0 | 1320 | 0.5717 | 0.4031 | 0.4962 | 0.6547 | 0.4677 |
| 0.1848 | 11.0 | 1452 | 0.5667 | 0.4043 | 0.4910 | 0.6609 | 0.4666 |
| 0.1096 | 12.0 | 1584 | 0.5691 | 0.4044 | 0.4949 | 0.6593 | 0.4688 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- May 24, 2024
- Updated
- May 24, 2024