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distilbert-finetuned-ner
McCurry/distilbert-finetuned-ner
distilbert-finetuned-ner is a token classification model from McCurry. 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
- May 9, 2024
- Updated
- May 9, 2024
- SHA
- 534d06fb
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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. -->
distilbert-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2266
- Precision: 0.9300
- Recall: 0.9306
- F1: 0.9303
- Accuracy: 0.9203
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 | 134 | 0.2481 | 0.9155 | 0.9305 | 0.9229 | 0.9146 |
| No log | 2.0 | 268 | 0.2355 | 0.9291 | 0.9278 | 0.9285 | 0.9177 |
| No log | 3.0 | 402 | 0.2266 | 0.9300 | 0.9306 | 0.9303 | 0.9203 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- May 9, 2024
- Updated
- May 9, 2024