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README.md
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---
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library_name: transformers
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license: mit
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base_model: BAAI/bge-small-en-v1.5
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: bert-finetuned-ner
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# bert-finetuned-ner
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This model is a fine-tuned version of [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1907
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- Precision: 0.8581
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- Recall: 0.9036
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- F1: 0.8802
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- Accuracy: 0.9774
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.3047 | 1.0 | 625 | 0.2442 | 0.8142 | 0.8869 | 0.8490 | 0.9710 |
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| 0.2064 | 2.0 | 1250 | 0.1974 | 0.8537 | 0.8995 | 0.8760 | 0.9767 |
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| 0.1724 | 3.0 | 1875 | 0.1907 | 0.8581 | 0.9036 | 0.8802 | 0.9774 |
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### Framework versions
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- Transformers 5.16.1
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- Pytorch 2.11.0+cu128
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- Datasets 4.8.5
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- Tokenizers 0.23.1
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