--- license: apache-2.0 base_model: bert-base-multilingual-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Bert_Text_Classification_v4 results: [] --- # Bert_Text_Classification_v4 This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0376 - Accuracy: 0.9964 - F1: 0.9963 - Precision: 0.9963 - Recall: 0.9963 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.0043 | 0.36 | 50 | 0.0399 | 0.9955 | 0.9953 | 0.9954 | 0.9951 | | 0.0001 | 0.72 | 100 | 0.0226 | 0.9964 | 0.9961 | 0.9962 | 0.9961 | | 0.0193 | 1.09 | 150 | 0.0668 | 0.9900 | 0.9893 | 0.9905 | 0.9884 | | 0.0555 | 1.45 | 200 | 0.0504 | 0.9927 | 0.9927 | 0.9934 | 0.9922 | | 0.0465 | 1.81 | 250 | 0.0017 | 0.9991 | 0.9990 | 0.9990 | 0.9991 | | 0.048 | 2.17 | 300 | 0.0348 | 0.9936 | 0.9934 | 0.9937 | 0.9932 | | 0.0513 | 2.54 | 350 | 0.0699 | 0.9873 | 0.9870 | 0.9878 | 0.9865 | | 0.0213 | 2.9 | 400 | 0.0495 | 0.9927 | 0.9926 | 0.9925 | 0.9928 | | 0.0427 | 3.26 | 450 | 0.0587 | 0.9936 | 0.9933 | 0.9939 | 0.9928 | | 0.0097 | 3.62 | 500 | 0.0236 | 0.9964 | 0.9961 | 0.9963 | 0.9959 | | 0.0001 | 3.99 | 550 | 0.0279 | 0.9964 | 0.9962 | 0.9964 | 0.9959 | | 0.0001 | 4.35 | 600 | 0.0259 | 0.9973 | 0.9972 | 0.9975 | 0.9968 | | 0.0 | 4.71 | 650 | 0.0260 | 0.9973 | 0.9972 | 0.9975 | 0.9968 | | 0.0091 | 5.07 | 700 | 0.0216 | 0.9964 | 0.9962 | 0.9964 | 0.9959 | | 0.0014 | 5.43 | 750 | 0.0268 | 0.9973 | 0.9972 | 0.9971 | 0.9972 | | 0.0 | 5.8 | 800 | 0.0383 | 0.9955 | 0.9952 | 0.9957 | 0.9947 | | 0.0 | 6.16 | 850 | 0.0362 | 0.9964 | 0.9962 | 0.9966 | 0.9958 | | 0.0003 | 6.52 | 900 | 0.0956 | 0.9909 | 0.9904 | 0.9900 | 0.9910 | | 0.0247 | 6.88 | 950 | 0.0285 | 0.9973 | 0.9972 | 0.9975 | 0.9968 | | 0.0003 | 7.25 | 1000 | 0.0333 | 0.9964 | 0.9962 | 0.9967 | 0.9958 | | 0.0001 | 7.61 | 1050 | 0.0334 | 0.9964 | 0.9962 | 0.9967 | 0.9958 | | 0.0003 | 7.97 | 1100 | 0.0285 | 0.9973 | 0.9972 | 0.9971 | 0.9972 | | 0.0001 | 8.33 | 1150 | 0.0294 | 0.9964 | 0.9962 | 0.9962 | 0.9962 | | 0.0 | 8.7 | 1200 | 0.0298 | 0.9964 | 0.9962 | 0.9962 | 0.9962 | | 0.0045 | 9.06 | 1250 | 0.0376 | 0.9955 | 0.9953 | 0.9954 | 0.9951 | | 0.0004 | 9.42 | 1300 | 0.0450 | 0.9946 | 0.9943 | 0.9943 | 0.9942 | | 0.0322 | 9.78 | 1350 | 0.0492 | 0.9936 | 0.9932 | 0.9939 | 0.9926 | | 0.003 | 10.14 | 1400 | 0.0110 | 0.9991 | 0.9991 | 0.9992 | 0.9989 | | 0.0001 | 10.51 | 1450 | 0.0112 | 0.9991 | 0.9991 | 0.9992 | 0.9989 | | 0.0001 | 10.87 | 1500 | 0.0124 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 11.23 | 1550 | 0.0112 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 11.59 | 1600 | 0.0111 | 0.9991 | 0.9991 | 0.9992 | 0.9989 | | 0.0 | 11.96 | 1650 | 0.0110 | 0.9991 | 0.9991 | 0.9992 | 0.9989 | | 0.0 | 12.32 | 1700 | 0.0110 | 0.9991 | 0.9991 | 0.9992 | 0.9989 | | 0.0 | 12.68 | 1750 | 0.0109 | 0.9991 | 0.9991 | 0.9992 | 0.9989 | | 0.0 | 13.04 | 1800 | 0.0109 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 13.41 | 1850 | 0.0109 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 13.77 | 1900 | 0.0109 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 14.13 | 1950 | 0.0109 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 14.49 | 2000 | 0.0109 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 14.86 | 2050 | 0.0109 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 15.22 | 2100 | 0.0109 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 15.58 | 2150 | 0.0110 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 15.94 | 2200 | 0.0110 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 16.3 | 2250 | 0.0110 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 16.67 | 2300 | 0.0111 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 17.03 | 2350 | 0.0111 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 17.39 | 2400 | 0.0111 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 17.75 | 2450 | 0.0112 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 18.12 | 2500 | 0.0112 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0 | 18.48 | 2550 | 0.0112 | 0.9991 | 0.9990 | 0.9991 | 0.9989 | | 0.0099 | 18.84 | 2600 | 0.0175 | 0.9973 | 0.9973 | 0.9973 | 0.9973 | | 0.0 | 19.2 | 2650 | 0.0133 | 0.9982 | 0.9981 | 0.9983 | 0.9979 | | 0.0 | 19.57 | 2700 | 0.0135 | 0.9982 | 0.9981 | 0.9983 | 0.9979 | | 0.0 | 19.93 | 2750 | 0.0135 | 0.9982 | 0.9981 | 0.9983 | 0.9979 | | 0.0 | 20.29 | 2800 | 0.0135 | 0.9982 | 0.9981 | 0.9983 | 0.9979 | | 0.0 | 20.65 | 2850 | 0.0132 | 0.9982 | 0.9981 | 0.9983 | 0.9979 | | 0.0 | 21.01 | 2900 | 0.0133 | 0.9982 | 0.9981 | 0.9983 | 0.9979 | | 0.0 | 21.38 | 2950 | 0.0133 | 0.9982 | 0.9981 | 0.9983 | 0.9979 | | 0.0 | 21.74 | 3000 | 0.0124 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 22.1 | 3050 | 0.0125 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 22.46 | 3100 | 0.0125 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 22.83 | 3150 | 0.0125 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 23.19 | 3200 | 0.0125 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 23.55 | 3250 | 0.0126 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 23.91 | 3300 | 0.0126 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 24.28 | 3350 | 0.0126 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 24.64 | 3400 | 0.0126 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 25.0 | 3450 | 0.0126 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 25.36 | 3500 | 0.0127 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 25.72 | 3550 | 0.0127 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 26.09 | 3600 | 0.0127 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 26.45 | 3650 | 0.0127 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 26.81 | 3700 | 0.0127 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 27.17 | 3750 | 0.0127 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 27.54 | 3800 | 0.0127 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 27.9 | 3850 | 0.0127 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 28.26 | 3900 | 0.0127 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 28.62 | 3950 | 0.0127 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 28.99 | 4000 | 0.0127 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 29.35 | 4050 | 0.0127 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | | 0.0 | 29.71 | 4100 | 0.0127 | 0.9982 | 0.9981 | 0.9981 | 0.9980 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.0+cu121 - Tokenizers 0.15.2