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try_connll-finetuned-ner
suwani/try_connll-finetuned-ner
try_connll-finetuned-ner is a token classification model from suwani. 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
- Sep 26, 2021
- SHA
- 9e5b394a
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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. -->
try_connll-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0596
- Precision: 0.9283
- Recall: 0.9372
- F1: 0.9328
- Accuracy: 0.9841
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 |
|---|---|---|---|---|---|---|---|
| 0.2383 | 1.0 | 878 | 0.0691 | 0.9139 | 0.9239 | 0.9189 | 0.9810 |
| 0.0497 | 2.0 | 1756 | 0.0607 | 0.9200 | 0.9343 | 0.9271 | 0.9833 |
| 0.0303 | 3.0 | 2634 | 0.0596 | 0.9283 | 0.9372 | 0.9328 | 0.9841 |
Framework versions
- Transformers 4.10.3
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Sep 26, 2021