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aleph_bert-finetuned-ner
msperka/aleph_bert-finetuned-ner
aleph_bert-finetuned-ner is a token classification model from msperka. 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
- bert
- Access
- Public
- Created
- Dec 14, 2023
- Updated
- Dec 14, 2023
- SHA
- aa98849e
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Base models
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. -->
aleph_bert-finetuned-ner
This model is a fine-tuned version of onlplab/alephbert-base on the nemo_corpus dataset. It achieves the following results on the evaluation set:
- Loss: 0.1408
- Precision: 0.8333
- Recall: 0.8262
- F1: 0.8298
- Accuracy: 0.9739
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 |
|---|---|---|---|---|---|---|---|
| 0.042 | 1.0 | 618 | 0.1317 | 0.8198 | 0.8068 | 0.8132 | 0.9720 |
| 0.0185 | 2.0 | 1236 | 0.1367 | 0.8224 | 0.8214 | 0.8219 | 0.9714 |
| 0.0185 | 3.0 | 1854 | 0.1408 | 0.8333 | 0.8262 | 0.8298 | 0.9739 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cpu
- Datasets 2.15.0
- Tokenizers 0.15.0
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Dec 14, 2023
- Updated
- Dec 14, 2023