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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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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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model-index: |
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- name: bert-base-uncased-finetuned-srl_arg |
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results: [] |
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--- |
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# bert-base-uncased-finetuned-srl_arg |
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This model is a baseline fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the English Universal Propbank dataset for the Semantics Role Labeling (SRL) task. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1094 |
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- Precision: 0.8207 |
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- Recall: 0.8310 |
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- F1: 0.8259 |
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- Accuracy: 0.9722 |
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## Model description |
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The appraoch used for the baseline model is basically converting the sentence into the following form: |
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> [CLS] This is the sentence content [SEP] is [SEP]. |
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And this is realized by simply using the logic of the auto tokenizer: `tokenizer(list1,list2)` will return [CLS] list1 content [SEP] list2 content [SEP]. |
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## Usages |
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The model labels semantics roles given input sentences. See usage examples at https://github.com/dannashao/bertsrl/blob/main/Evaluation.ipynb |
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## Training and evaluation data |
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The English Universal Proposition Bank v1.0 data. See details at https://github.com/UniversalPropositions/UP-1.0 |
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## Training procedure |
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See details at https://github.com/chuqiaog/Advanced_NLP_group_1/blob/main/A3/A3_main.ipynb |
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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: 3 |
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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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| 0.1082 | 1.0 | 2655 | 0.1236 | 0.7783 | 0.8158 | 0.7966 | 0.9671 | |
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| 0.0772 | 2.0 | 5310 | 0.1089 | 0.8055 | 0.8277 | 0.8165 | 0.9708 | |
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| 0.0609 | 3.0 | 7965 | 0.1094 | 0.8207 | 0.8310 | 0.8259 | 0.9722 | |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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