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wikigold_trained_no_DA_testing
DOOGLAK/wikigold_trained_no_DA_testing
wikigold_trained_no_DA_testing is a token classification model from DOOGLAK. 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. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Aug 4, 2022
- Updated
- Aug 4, 2022
- SHA
- 1bf3ee26
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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. -->
wikigold_trained_no_DA_testing
This model is a fine-tuned version of bert-base-cased on the wikigold_splits dataset. It achieves the following results on the evaluation set:
- Loss: 0.1490
- Precision: 0.8380
- Recall: 0.8490
- F1: 0.8435
- Accuracy: 0.9564
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 167 | 0.1697 | 0.7384 | 0.7461 | 0.7422 | 0.9409 |
| No log | 2.0 | 334 | 0.1526 | 0.8101 | 0.8112 | 0.8107 | 0.9515 |
| 0.1553 | 3.0 | 501 | 0.1490 | 0.8380 | 0.8490 | 0.8435 | 0.9564 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Aug 4, 2022
- Updated
- Aug 4, 2022