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bert-base-multilingual-cased-finetuned-multilingual-pos
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1999
- Precision: 0.9438
- Recall: 0.9438
- F1: 0.9438
- Accuracy: 0.9541
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
1.0385 | 0.29 | 100 | 0.4411 | 0.8523 | 0.8473 | 0.8498 | 0.8739 |
0.3849 | 0.57 | 200 | 0.3275 | 0.8907 | 0.8913 | 0.8910 | 0.9103 |
0.2976 | 0.86 | 300 | 0.2879 | 0.9034 | 0.9037 | 0.9036 | 0.9203 |
0.2487 | 1.14 | 400 | 0.2599 | 0.9132 | 0.9115 | 0.9123 | 0.9285 |
0.2027 | 1.43 | 500 | 0.2444 | 0.9224 | 0.9198 | 0.9211 | 0.9349 |
0.1899 | 1.71 | 600 | 0.2287 | 0.9239 | 0.9246 | 0.9243 | 0.9378 |
0.18 | 2.0 | 700 | 0.2184 | 0.9282 | 0.9297 | 0.9289 | 0.9418 |
0.1351 | 2.29 | 800 | 0.2214 | 0.9297 | 0.9291 | 0.9294 | 0.9424 |
0.134 | 2.57 | 900 | 0.2123 | 0.9337 | 0.9333 | 0.9335 | 0.9458 |
0.1294 | 2.86 | 1000 | 0.1993 | 0.9359 | 0.9344 | 0.9352 | 0.9476 |
0.1156 | 3.14 | 1100 | 0.2018 | 0.9377 | 0.9377 | 0.9377 | 0.9494 |
0.1007 | 3.43 | 1200 | 0.2027 | 0.9375 | 0.9384 | 0.9380 | 0.9495 |
0.0959 | 3.71 | 1300 | 0.1971 | 0.9387 | 0.9394 | 0.9390 | 0.9505 |
0.0982 | 4.0 | 1400 | 0.1953 | 0.9408 | 0.9414 | 0.9411 | 0.9522 |
0.0761 | 4.29 | 1500 | 0.1987 | 0.9404 | 0.9412 | 0.9408 | 0.9517 |
0.0788 | 4.57 | 1600 | 0.1994 | 0.9405 | 0.9411 | 0.9408 | 0.9518 |
0.0755 | 4.86 | 1700 | 0.2009 | 0.9413 | 0.9420 | 0.9417 | 0.9525 |
0.0671 | 5.14 | 1800 | 0.2011 | 0.9421 | 0.9423 | 0.9422 | 0.9527 |
0.0636 | 5.43 | 1900 | 0.2002 | 0.9428 | 0.9431 | 0.9430 | 0.9532 |
0.0628 | 5.71 | 2000 | 0.1993 | 0.9422 | 0.9433 | 0.9428 | 0.9532 |
0.0645 | 6.0 | 2100 | 0.1979 | 0.9434 | 0.9430 | 0.9432 | 0.9536 |
0.0543 | 6.29 | 2200 | 0.2017 | 0.9427 | 0.9434 | 0.9430 | 0.9532 |
0.0558 | 6.57 | 2300 | 0.1992 | 0.9427 | 0.9432 | 0.9430 | 0.9534 |
0.0529 | 6.86 | 2400 | 0.1999 | 0.9438 | 0.9438 | 0.9438 | 0.9541 |
Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1