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--- |
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base_model: SALT-NLP/FLANG-BERT |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: FLANG-BERT_Synonym-wordnet |
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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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# FLANG-BERT_Synonym-wordnet |
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This model is a fine-tuned version of [SALT-NLP/FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2403 |
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- Accuracy: 0.9189 |
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- F1: 0.9190 |
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- Precision: 0.9194 |
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- Recall: 0.9189 |
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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: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 25 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.7933 | 1.0 | 91 | 0.7348 | 0.6880 | 0.6790 | 0.6972 | 0.6880 | |
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| 0.392 | 2.0 | 182 | 0.3757 | 0.8456 | 0.8461 | 0.8488 | 0.8456 | |
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| 0.2646 | 3.0 | 273 | 0.3044 | 0.8846 | 0.8839 | 0.8876 | 0.8846 | |
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| 0.1867 | 4.0 | 364 | 0.3522 | 0.8846 | 0.8846 | 0.8910 | 0.8846 | |
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| 0.1158 | 5.0 | 455 | 0.2403 | 0.9189 | 0.9190 | 0.9194 | 0.9189 | |
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| 0.1375 | 6.0 | 546 | 0.3255 | 0.9111 | 0.9105 | 0.9108 | 0.9111 | |
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| 0.1311 | 7.0 | 637 | 0.4090 | 0.8924 | 0.8930 | 0.9028 | 0.8924 | |
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| 0.1054 | 8.0 | 728 | 0.4269 | 0.9017 | 0.9015 | 0.9023 | 0.9017 | |
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| 0.0739 | 9.0 | 819 | 0.4098 | 0.9002 | 0.8992 | 0.9008 | 0.9002 | |
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| 0.0389 | 10.0 | 910 | 0.4411 | 0.9033 | 0.9030 | 0.9072 | 0.9033 | |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.1 |
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