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bert_ner_output
keatrean/bert_ner_output
bert_ner_output is a token classification model from keatrean. 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
- Jan 13, 2025
- Updated
- Jan 13, 2025
- SHA
- 3ae3d65b
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Base models
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_ner_output
This model is a fine-tuned version of DeepPavlov/rubert-base-cased-conversational on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0081
- Precision: 0.9301
- Recall: 0.9323
- F1: 0.9312
- Accuracy: 0.9980
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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0062 | 1.0 | 6119 | 0.0076 | 0.9353 | 0.9097 | 0.9223 | 0.9978 |
| 0.0284 | 2.0 | 12238 | 0.0073 | 0.9396 | 0.9186 | 0.9290 | 0.9980 |
| 0.0022 | 3.0 | 18357 | 0.0081 | 0.9301 | 0.9323 | 0.9312 | 0.9980 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- Created
- Jan 13, 2025
- Updated
- Jan 13, 2025