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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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<!-- 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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# roberta-large-finetuned-ner |
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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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## 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: 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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.8259 | 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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### Framework versions |
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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 |
