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robbert1010_lrate10b16
Tommert25/robbert1010_lrate10b16
robbert1010_lrate10b16 is a token classification model from Tommert25. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Oct 10, 2023
- Updated
- Oct 10, 2023
- SHA
- 4231f7c9
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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. -->
robbert1010_lrate10b16
This model is a fine-tuned version of pdelobelle/robbert-v2-dutch-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6021
- Precisions: 0.8323
- Recall: 0.7951
- F-measure: 0.8088
- Accuracy: 0.9164
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: 0.0001
- 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: 14
Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.6104 | 1.0 | 236 | 0.4304 | 0.8532 | 0.6700 | 0.6852 | 0.8707 |
| 0.32 | 2.0 | 472 | 0.3520 | 0.7761 | 0.7551 | 0.7413 | 0.8916 |
| 0.1989 | 3.0 | 708 | 0.3686 | 0.7500 | 0.7591 | 0.7465 | 0.9010 |
| 0.1331 | 4.0 | 944 | 0.4045 | 0.8289 | 0.7666 | 0.7835 | 0.9090 |
| 0.0784 | 5.0 | 1180 | 0.4307 | 0.8052 | 0.7759 | 0.7890 | 0.9092 |
| 0.0682 | 6.0 | 1416 | 0.4696 | 0.8101 | 0.7658 | 0.7770 | 0.9059 |
| 0.04 | 7.0 | 1652 | 0.5078 | 0.8450 | 0.7642 | 0.7820 | 0.9096 |
| 0.0256 | 8.0 | 1888 | 0.5718 | 0.8007 | 0.7830 | 0.7906 | 0.9058 |
| 0.0219 | 9.0 | 2124 | 0.5508 | 0.8078 | 0.7987 | 0.8000 | 0.9093 |
| 0.0162 | 10.0 | 2360 | 0.5786 | 0.8256 | 0.7791 | 0.7946 | 0.9141 |
| 0.0117 | 11.0 | 2596 | 0.5979 | 0.8360 | 0.7912 | 0.8046 | 0.9168 |
| 0.011 | 12.0 | 2832 | 0.6021 | 0.8323 | 0.7951 | 0.8088 | 0.9164 |
| 0.0079 | 13.0 | 3068 | 0.6115 | 0.8337 | 0.7956 | 0.8088 | 0.9166 |
| 0.0064 | 14.0 | 3304 | 0.6100 | 0.8305 | 0.7932 | 0.8064 | 0.9164 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
- Task
- Token Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Oct 10, 2023
- Updated
- Oct 10, 2023