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distilbert-base-uncased-finetuned-ner
mcdzwil/distilbert-base-uncased-finetuned-ner
distilbert-base-uncased-finetuned-ner is a token classification model from mcdzwil. 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
- Mar 2, 2022
- Updated
- Mar 2, 2022
- SHA
- bb59e317
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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-base-uncased-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: 0.1830
- Precision: 0.9171
- Recall: 0.7099
- F1: 0.8003
- Accuracy: 0.9316
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 | 48 | 0.2903 | 0.7952 | 0.7063 | 0.7481 | 0.9136 |
| No log | 2.0 | 96 | 0.2015 | 0.9154 | 0.7075 | 0.7981 | 0.9298 |
| No log | 3.0 | 144 | 0.1830 | 0.9171 | 0.7099 | 0.8003 | 0.9316 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Mar 2, 2022