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--- |
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license: apache-2.0 |
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base_model: dslim/distilbert-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: distilbert-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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# distilbert-finetuned-ner |
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This model is a fine-tuned version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4109 |
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- Precision: 0.6952 |
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- Recall: 0.7549 |
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- F1: 0.7238 |
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- Accuracy: 0.8724 |
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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: 1e-06 |
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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: 20 |
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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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| 1.647 | 1.0 | 977 | 0.8265 | 0.4918 | 0.5741 | 0.5298 | 0.7793 | |
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| 0.7697 | 2.0 | 1954 | 0.6350 | 0.5801 | 0.6567 | 0.6160 | 0.8194 | |
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| 0.6089 | 3.0 | 2931 | 0.5591 | 0.6138 | 0.6857 | 0.6478 | 0.8352 | |
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| 0.534 | 4.0 | 3908 | 0.5163 | 0.6296 | 0.6955 | 0.6609 | 0.8439 | |
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| 0.4911 | 5.0 | 4885 | 0.4885 | 0.6436 | 0.7075 | 0.6740 | 0.8498 | |
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| 0.4545 | 6.0 | 5862 | 0.4683 | 0.6526 | 0.7165 | 0.6830 | 0.8557 | |
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| 0.4379 | 7.0 | 6839 | 0.4534 | 0.6600 | 0.7231 | 0.6901 | 0.8592 | |
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| 0.4124 | 8.0 | 7816 | 0.4441 | 0.6713 | 0.7274 | 0.6982 | 0.8625 | |
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| 0.403 | 9.0 | 8793 | 0.4345 | 0.6746 | 0.7359 | 0.7039 | 0.8658 | |
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| 0.394 | 10.0 | 9770 | 0.4324 | 0.6835 | 0.7445 | 0.7127 | 0.8667 | |
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| 0.3782 | 11.0 | 10747 | 0.4256 | 0.6820 | 0.7465 | 0.7128 | 0.8678 | |
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| 0.3706 | 12.0 | 11724 | 0.4213 | 0.6873 | 0.7460 | 0.7155 | 0.8691 | |
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| 0.3712 | 13.0 | 12701 | 0.4197 | 0.6873 | 0.7518 | 0.7181 | 0.8703 | |
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| 0.3626 | 14.0 | 13678 | 0.4163 | 0.6882 | 0.7523 | 0.7188 | 0.8713 | |
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| 0.351 | 15.0 | 14655 | 0.4142 | 0.6905 | 0.7528 | 0.7203 | 0.8717 | |
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| 0.3528 | 16.0 | 15632 | 0.4142 | 0.6932 | 0.7538 | 0.7222 | 0.8718 | |
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| 0.3523 | 17.0 | 16609 | 0.4123 | 0.6949 | 0.7533 | 0.7229 | 0.8722 | |
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| 0.3464 | 18.0 | 17586 | 0.4107 | 0.6936 | 0.7538 | 0.7224 | 0.8727 | |
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| 0.342 | 19.0 | 18563 | 0.4115 | 0.6954 | 0.7560 | 0.7244 | 0.8726 | |
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| 0.3496 | 20.0 | 19540 | 0.4109 | 0.6952 | 0.7549 | 0.7238 | 0.8724 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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