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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wikiann |
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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: deberta-finetuned-ner-connll-late-stop |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: wikiann |
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type: wikiann |
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config: en |
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split: train |
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args: en |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.830192600803658 |
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- name: Recall |
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type: recall |
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value: 0.8470945850417079 |
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- name: F1 |
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type: f1 |
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value: 0.8385584324702589 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9228861596598961 |
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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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# deberta-finetuned-ner-connll-late-stop |
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This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the wikiann dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5259 |
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- Precision: 0.8302 |
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- Recall: 0.8471 |
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- F1: 0.8386 |
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- Accuracy: 0.9229 |
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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: 7 |
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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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| 0.3408 | 1.0 | 1875 | 0.3639 | 0.7462 | 0.7887 | 0.7669 | 0.8966 | |
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| 0.2435 | 2.0 | 3750 | 0.2933 | 0.8104 | 0.8332 | 0.8217 | 0.9178 | |
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| 0.1822 | 3.0 | 5625 | 0.3034 | 0.8147 | 0.8388 | 0.8266 | 0.9221 | |
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| 0.1402 | 4.0 | 7500 | 0.3667 | 0.8275 | 0.8474 | 0.8374 | 0.9235 | |
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| 0.1013 | 5.0 | 9375 | 0.4290 | 0.8285 | 0.8448 | 0.8366 | 0.9227 | |
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| 0.0677 | 6.0 | 11250 | 0.4914 | 0.8259 | 0.8473 | 0.8365 | 0.9231 | |
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| 0.0439 | 7.0 | 13125 | 0.5259 | 0.8302 | 0.8471 | 0.8386 | 0.9229 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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