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finetuned-ner
kolj4/finetuned-ner
finetuned-ner is a token classification model from kolj4. 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
- Nov 27, 2022
- Updated
- Nov 28, 2022
- SHA
- 49bbc1fe
Try a prompt
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. -->
finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2041
- Precision: 0.8442
- Recall: 0.8460
- F1: 0.8451
- Accuracy: 0.9398
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 |
|---|---|---|---|---|---|---|---|
| 0.1913 | 1.0 | 16472 | 0.1915 | 0.8375 | 0.8414 | 0.8395 | 0.9376 |
| 0.1544 | 2.0 | 32944 | 0.1928 | 0.8357 | 0.8515 | 0.8435 | 0.9387 |
| 0.1231 | 3.0 | 49416 | 0.2041 | 0.8442 | 0.8460 | 0.8451 | 0.9398 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Nov 27, 2022
- Updated
- Nov 28, 2022