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AraBERT_token_classification_merged
MM2157/AraBERT_token_classification_merged
AraBERT_token_classification_merged is a token classification model from MM2157. Use it when you need labels on individual words, such as names. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Mar 26, 2024
- Updated
- Mar 26, 2024
- SHA
- 498fb2f1
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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. -->
AraBERT_token_classification_merged
This model is a fine-tuned version of aubmindlab/bert-base-arabert on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4477
- Precision: 0.0264
- Recall: 0.0071
- F1: 0.0112
- Accuracy: 0.6557
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 254 | 1.4827 | 0.0 | 0.0 | 0.0 | 0.6786 |
| 1.2057 | 2.0 | 508 | 1.4545 | 0.0 | 0.0 | 0.0 | 0.6789 |
| 1.2057 | 3.0 | 762 | 1.4587 | 0.0022 | 0.0005 | 0.0008 | 0.6553 |
| 0.9359 | 4.0 | 1016 | 1.4667 | 0.0283 | 0.0041 | 0.0071 | 0.6686 |
| 0.9359 | 5.0 | 1270 | 1.4477 | 0.0264 | 0.0071 | 0.0112 | 0.6557 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.12.1
- Datasets 2.13.2
- Tokenizers 0.13.3
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- Created
- Mar 26, 2024
- Updated
- Mar 26, 2024