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bert-base-irish-cased-v1-finetuned-ner
jimregan/bert-base-irish-cased-v1-finetuned-ner
bert-base-irish-cased-v1-finetuned-ner is a token classification model from jimregan. 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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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. -->
bert-base-irish-cased-v1-finetuned-ner
This model is a fine-tuned version of DCU-NLP/bert-base-irish-cased-v1 on the wikiann dataset. It achieves the following results on the evaluation set:
- Loss: 0.2468
- Precision: 0.8191
- Recall: 0.8363
- F1: 0.8276
- Accuracy: 0.9307
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 63 | 0.4902 | 0.5579 | 0.5269 | 0.5420 | 0.8458 |
| No log | 2.0 | 126 | 0.3227 | 0.7169 | 0.7417 | 0.7291 | 0.8991 |
| No log | 3.0 | 189 | 0.2720 | 0.7895 | 0.7839 | 0.7867 | 0.9186 |
| No log | 4.0 | 252 | 0.2585 | 0.8128 | 0.8296 | 0.8211 | 0.9264 |
| No log | 5.0 | 315 | 0.2468 | 0.8191 | 0.8363 | 0.8276 | 0.9307 |
Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Languages
- ga
- Created
- Mar 2, 2022
- Updated
- Dec 21, 2023