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Article_50v0_NER_Model_3Epochs
DOOGLAK/Article_50v0_NER_Model_3Epochs
Article_50v0_NER_Model_3Epochs 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 11, 2022
- Updated
- Aug 11, 2022
- SHA
- d232eb14
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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. -->
Article_50v0_NER_Model_3Epochs
This model is a fine-tuned version of bert-base-cased on the article50v0_wikigold_split dataset. It achieves the following results on the evaluation set:
- Loss: 0.6128
- Precision: 0.1232
- Recall: 0.0144
- F1: 0.0258
- Accuracy: 0.7887
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 | 14 | 0.7610 | 0.0 | 0.0 | 0.0 | 0.7805 |
| No log | 2.0 | 28 | 0.6486 | 0.0905 | 0.0051 | 0.0097 | 0.7849 |
| No log | 3.0 | 42 | 0.6128 | 0.1232 | 0.0144 | 0.0258 | 0.7887 |
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 11, 2022
- Updated
- Aug 11, 2022