Downloads · 30 days
6
13% of all-time downloads
bert-base-multilingual-cased-finetuned-ner-geocorpus
GuiTap/bert-base-multilingual-cased-finetuned-ner-geocorpus
bert-base-multilingual-cased-finetuned-ner-geocorpus is a token classification model from GuiTap. 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
46
Public
Parameters
177M
11.3 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
- Jun 9, 2024
- Updated
- Oct 6, 2024
- SHA
- 2e13539d
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-base-multilingual-cased-finetuned-ner-geocorpus
This model is a fine-tuned version of google-bert/bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1098
- Precision: 0.8046
- Recall: 0.8681
- F1: 0.8352
- Accuracy: 0.9718
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: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 276 | 0.1803 | 0.7390 | 0.6469 | 0.6899 | 0.9556 |
| 0.2476 | 2.0 | 552 | 0.1196 | 0.8330 | 0.7767 | 0.8039 | 0.9699 |
| 0.2476 | 3.0 | 828 | 0.1157 | 0.8719 | 0.7778 | 0.8222 | 0.9717 |
| 0.0766 | 4.0 | 1104 | 0.1229 | 0.7866 | 0.8806 | 0.8310 | 0.9717 |
| 0.0766 | 5.0 | 1380 | 0.1105 | 0.8567 | 0.8692 | 0.8629 | 0.9761 |
| 0.0393 | 6.0 | 1656 | 0.1098 | 0.8046 | 0.8681 | 0.8352 | 0.9718 |
| 0.0393 | 7.0 | 1932 | 0.1180 | 0.8663 | 0.8744 | 0.8703 | 0.9786 |
| 0.0236 | 8.0 | 2208 | 0.1294 | 0.8293 | 0.8982 | 0.8624 | 0.9744 |
| 0.0236 | 9.0 | 2484 | 0.1337 | 0.8864 | 0.8505 | 0.8680 | 0.9781 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Jun 9, 2024
- Updated
- Oct 6, 2024