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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_bert-base-uncased |
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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_bert-base-uncased |
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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: 0.7138 |
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- Accuracy: 0.8643 |
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- F1: 0.8645 |
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- Precision: 0.8681 |
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- Recall: 0.8643 |
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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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| 0.8614 | 1.0 | 91 | 0.8043 | 0.6443 | 0.6279 | 0.6398 | 0.6443 | |
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| 0.5386 | 2.0 | 182 | 0.4807 | 0.8112 | 0.8113 | 0.8150 | 0.8112 | |
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| 0.3422 | 3.0 | 273 | 0.4452 | 0.8300 | 0.8304 | 0.8351 | 0.8300 | |
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| 0.2617 | 4.0 | 364 | 0.5424 | 0.8190 | 0.8177 | 0.8259 | 0.8190 | |
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| 0.164 | 5.0 | 455 | 0.5162 | 0.8424 | 0.8414 | 0.8424 | 0.8424 | |
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| 0.1278 | 6.0 | 546 | 0.5737 | 0.8440 | 0.8439 | 0.8440 | 0.8440 | |
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| 0.0599 | 7.0 | 637 | 0.6869 | 0.8268 | 0.8236 | 0.8311 | 0.8268 | |
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| 0.1184 | 8.0 | 728 | 0.5331 | 0.8471 | 0.8475 | 0.8493 | 0.8471 | |
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| 0.1126 | 9.0 | 819 | 0.6979 | 0.8237 | 0.8221 | 0.8332 | 0.8237 | |
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| 0.0737 | 10.0 | 910 | 0.7481 | 0.8362 | 0.8362 | 0.8381 | 0.8362 | |
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| 0.1425 | 11.0 | 1001 | 0.7602 | 0.8315 | 0.8308 | 0.8331 | 0.8315 | |
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| 0.0666 | 12.0 | 1092 | 0.6645 | 0.8612 | 0.8612 | 0.8615 | 0.8612 | |
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| 0.0523 | 13.0 | 1183 | 0.7138 | 0.8643 | 0.8645 | 0.8681 | 0.8643 | |
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| 0.0168 | 14.0 | 1274 | 0.7317 | 0.8534 | 0.8525 | 0.8527 | 0.8534 | |
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| 0.0336 | 15.0 | 1365 | 0.8575 | 0.8456 | 0.8454 | 0.8553 | 0.8456 | |
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| 0.0424 | 16.0 | 1456 | 0.9331 | 0.8409 | 0.8386 | 0.8423 | 0.8409 | |
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| 0.0188 | 17.0 | 1547 | 0.7885 | 0.8596 | 0.8595 | 0.8599 | 0.8596 | |
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| 0.0032 | 18.0 | 1638 | 0.8774 | 0.8596 | 0.8584 | 0.8592 | 0.8596 | |
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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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