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--- |
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license: mit |
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base_model: dslim/bert-large-NER |
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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: Adam-NER-Model |
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results: [] |
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datasets: |
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- conll2003 |
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- rungalileo/mit_movies |
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- hyperhustle/ner-dataset |
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language: |
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- en |
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pipeline_tag: token-classification |
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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-adam |
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This model is a fine-tuned version of [dslim/bert-large-NER](https://huggingface.co/dslim/bert-large-NER) on an [hyperhustle/ner-dataset](https://huggingface.co/datasets/hyperhustle/ner-dataset) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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- Precision: 0.8845 |
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- Recall: 0.8749 |
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- F1: 0.8797 |
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- Accuracy: 0.9646 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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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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| 0.0949 | 1.0 | 3080 | nan | 0.8914 | 0.8942 | 0.8928 | 0.9663 | |
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| 0.0574 | 2.0 | 6160 | nan | 0.8763 | 0.8784 | 0.8773 | 0.9635 | |
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| 0.0376 | 3.0 | 9240 | nan | 0.8845 | 0.8749 | 0.8797 | 0.9646 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
