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--- |
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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: tajroberto-ner |
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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: tg |
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split: train+test |
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args: tg |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.3155080213903743 |
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- name: Recall |
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type: recall |
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value: 0.5673076923076923 |
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- name: F1 |
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type: f1 |
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value: 0.4054982817869416 |
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- name: Accuracy |
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type: accuracy |
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value: 0.83597621407334 |
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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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# tajroberto-ner |
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This model is a fine-tuned version of [muhtasham/RoBERTa-tg](https://huggingface.co./muhtasham/RoBERTa-tg) on the wikiann dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9408 |
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- Precision: 0.3155 |
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- Recall: 0.5673 |
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- F1: 0.4055 |
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- Accuracy: 0.8360 |
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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: 200 |
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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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| No log | 2.0 | 50 | 0.7710 | 0.0532 | 0.1827 | 0.0824 | 0.6933 | |
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| No log | 4.0 | 100 | 0.5901 | 0.0847 | 0.25 | 0.1265 | 0.7825 | |
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| No log | 6.0 | 150 | 0.5226 | 0.2087 | 0.4615 | 0.2874 | 0.8186 | |
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| No log | 8.0 | 200 | 0.5041 | 0.2585 | 0.5096 | 0.3430 | 0.8449 | |
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| No log | 10.0 | 250 | 0.5592 | 0.2819 | 0.5096 | 0.3630 | 0.8499 | |
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| No log | 12.0 | 300 | 0.5725 | 0.3032 | 0.5481 | 0.3904 | 0.8558 | |
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| No log | 14.0 | 350 | 0.6433 | 0.3122 | 0.5673 | 0.4027 | 0.8508 | |
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| No log | 16.0 | 400 | 0.6744 | 0.3543 | 0.5962 | 0.4444 | 0.8553 | |
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| No log | 18.0 | 450 | 0.7617 | 0.3353 | 0.5577 | 0.4188 | 0.8335 | |
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| 0.2508 | 20.0 | 500 | 0.7608 | 0.3262 | 0.5865 | 0.4192 | 0.8419 | |
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| 0.2508 | 22.0 | 550 | 0.8483 | 0.3224 | 0.5673 | 0.4111 | 0.8494 | |
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| 0.2508 | 24.0 | 600 | 0.8370 | 0.3275 | 0.5385 | 0.4073 | 0.8439 | |
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| 0.2508 | 26.0 | 650 | 0.8652 | 0.3410 | 0.5673 | 0.4260 | 0.8394 | |
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| 0.2508 | 28.0 | 700 | 0.9441 | 0.3409 | 0.5769 | 0.4286 | 0.8216 | |
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| 0.2508 | 30.0 | 750 | 0.9228 | 0.3333 | 0.5577 | 0.4173 | 0.8439 | |
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| 0.2508 | 32.0 | 800 | 0.9175 | 0.3430 | 0.5673 | 0.4275 | 0.8355 | |
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| 0.2508 | 34.0 | 850 | 0.9603 | 0.3073 | 0.5288 | 0.3887 | 0.8340 | |
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| 0.2508 | 36.0 | 900 | 0.9417 | 0.3240 | 0.5577 | 0.4099 | 0.8370 | |
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| 0.2508 | 38.0 | 950 | 0.9408 | 0.3155 | 0.5673 | 0.4055 | 0.8360 | |
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### Framework versions |
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- Transformers 4.21.2 |
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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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