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End of training

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README.md ADDED
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+ ---
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+ license: mit
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+ library_name: peft
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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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+ base_model: roberta-large
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+ model-index:
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+ - name: roberta-large-finetuned-ner
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+ results: []
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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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+ # roberta-large-finetuned-ner
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+
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+ This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0828
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+ - Precision: 0.9043
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+ - Recall: 0.9245
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+ - F1: 0.9143
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+ - Accuracy: 0.9793
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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: linear
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+ - num_epochs: 10
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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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+ | 0.8259 | 1.0 | 878 | 0.2398 | 0.6827 | 0.7083 | 0.6953 | 0.9371 |
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+ | 0.2115 | 2.0 | 1756 | 0.1560 | 0.8021 | 0.8172 | 0.8096 | 0.9600 |
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+ | 0.1612 | 3.0 | 2634 | 0.1274 | 0.8589 | 0.8506 | 0.8547 | 0.9672 |
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+ | 0.124 | 4.0 | 3512 | 0.1081 | 0.8832 | 0.8793 | 0.8813 | 0.9722 |
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+ | 0.1183 | 5.0 | 4390 | 0.0993 | 0.8910 | 0.9036 | 0.8973 | 0.9754 |
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+ | 0.1074 | 6.0 | 5268 | 0.0921 | 0.8974 | 0.9119 | 0.9046 | 0.9773 |
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+ | 0.1004 | 7.0 | 6146 | 0.0874 | 0.8983 | 0.9156 | 0.9068 | 0.9780 |
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+ | 0.0967 | 8.0 | 7024 | 0.0846 | 0.9028 | 0.9227 | 0.9127 | 0.9792 |
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+ | 0.0923 | 9.0 | 7902 | 0.0829 | 0.9039 | 0.9239 | 0.9138 | 0.9795 |
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+ | 0.0884 | 10.0 | 8780 | 0.0828 | 0.9043 | 0.9245 | 0.9143 | 0.9793 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.8.2
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+ - Transformers 4.37.2
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+ - Pytorch 2.2.0
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+ - Datasets 2.17.0
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+ - Tokenizers 0.15.2
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