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update model card README.md

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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - wnut_17
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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: albert-large-v2_ner_wnut_17
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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: wnut_17
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+ type: wnut_17
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+ args: wnut_17
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.7445742904841403
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+ - name: Recall
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+ type: recall
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+ value: 0.5334928229665071
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+ - name: F1
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+ type: f1
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+ value: 0.621602787456446
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9581637843336724
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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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+ # albert-large-v2_ner_wnut_17
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+
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+ This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the wnut_17 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2429
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+ - Precision: 0.7446
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+ - Recall: 0.5335
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+ - F1: 0.6216
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+ - Accuracy: 0.9582
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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: 16
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+ - eval_batch_size: 16
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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: cosine
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+ - num_epochs: 5
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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 | 1.0 | 213 | 0.3051 | 0.7929 | 0.3206 | 0.4566 | 0.9410 |
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+ | No log | 2.0 | 426 | 0.2151 | 0.7443 | 0.4665 | 0.5735 | 0.9516 |
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+ | 0.17 | 3.0 | 639 | 0.2310 | 0.7364 | 0.5012 | 0.5964 | 0.9559 |
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+ | 0.17 | 4.0 | 852 | 0.2387 | 0.7564 | 0.5311 | 0.6240 | 0.9578 |
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+ | 0.0587 | 5.0 | 1065 | 0.2429 | 0.7446 | 0.5335 | 0.6216 | 0.9582 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.20.1
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+ - Pytorch 1.11.0
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+ - Datasets 2.1.0
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+ - Tokenizers 0.12.1