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my_ner_model
balciberin/my_ner_model
my_ner_model is a token classification model from balciberin. 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
- distilbert
- Access
- Public
- Created
- Jun 8, 2024
- Updated
- Jun 8, 2024
- SHA
- b00efc3d
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Base models
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. -->
my_ner_model
This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2725
- Precision: 0.5482
- Recall: 0.3318
- F1: 0.4134
- Accuracy: 0.9440
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2869 | 0.4186 | 0.1835 | 0.2552 | 0.9356 |
| No log | 2.0 | 426 | 0.2692 | 0.5294 | 0.3086 | 0.3899 | 0.9428 |
| 0.1994 | 3.0 | 639 | 0.2725 | 0.5482 | 0.3318 | 0.4134 | 0.9440 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Jun 8, 2024
- Updated
- Jun 8, 2024