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muhtasham
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tajroberto-ner

Token Classification
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roberta
Generated from Trainer
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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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+
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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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+
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+ # tajroberto-ner
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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