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---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Intent-classification-12kv2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Intent-classification-12kv2
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0074
- Accuracy: 0.9984
- F1: 0.9983
- Precision: 0.9983
- Recall: 0.9983
## 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: 32
- 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: 10
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.742 | 0.05 | 10 | 1.4822 | 0.6954 | 0.6918 | 0.7288 | 0.6966 |
| 1.2849 | 0.11 | 20 | 0.9533 | 0.8713 | 0.8699 | 0.8899 | 0.8729 |
| 0.8226 | 0.16 | 30 | 0.5235 | 0.9786 | 0.9786 | 0.9790 | 0.9785 |
| 0.399 | 0.21 | 40 | 0.2295 | 0.9812 | 0.9812 | 0.9811 | 0.9817 |
| 0.1871 | 0.26 | 50 | 0.1168 | 0.9839 | 0.9839 | 0.9844 | 0.9836 |
| 0.0855 | 0.32 | 60 | 0.0508 | 0.9928 | 0.9928 | 0.9928 | 0.9928 |
| 0.0546 | 0.37 | 70 | 0.0300 | 0.9947 | 0.9947 | 0.9948 | 0.9947 |
| 0.0226 | 0.42 | 80 | 0.0271 | 0.9947 | 0.9948 | 0.9947 | 0.9948 |
| 0.0306 | 0.47 | 90 | 0.0416 | 0.9888 | 0.9887 | 0.9894 | 0.9883 |
| 0.0336 | 0.53 | 100 | 0.0157 | 0.9970 | 0.9970 | 0.9970 | 0.9971 |
| 0.0373 | 0.58 | 110 | 0.0214 | 0.9951 | 0.9951 | 0.9952 | 0.9951 |
| 0.0094 | 0.63 | 120 | 0.0121 | 0.9970 | 0.9971 | 0.9971 | 0.9970 |
| 0.0077 | 0.68 | 130 | 0.0094 | 0.9980 | 0.9980 | 0.9980 | 0.9981 |
| 0.0253 | 0.74 | 140 | 0.0077 | 0.9987 | 0.9987 | 0.9987 | 0.9987 |
| 0.0233 | 0.79 | 150 | 0.0075 | 0.9987 | 0.9987 | 0.9987 | 0.9987 |
| 0.0068 | 0.84 | 160 | 0.0080 | 0.9987 | 0.9987 | 0.9987 | 0.9987 |
| 0.0286 | 0.89 | 170 | 0.0141 | 0.9964 | 0.9964 | 0.9964 | 0.9964 |
| 0.0139 | 0.95 | 180 | 0.0104 | 0.9970 | 0.9970 | 0.9970 | 0.9971 |
| 0.0043 | 1.0 | 190 | 0.0074 | 0.9977 | 0.9977 | 0.9977 | 0.9976 |
| 0.0122 | 1.05 | 200 | 0.0065 | 0.9987 | 0.9987 | 0.9987 | 0.9987 |
| 0.0071 | 1.11 | 210 | 0.0059 | 0.9980 | 0.9980 | 0.9981 | 0.9980 |
| 0.0025 | 1.16 | 220 | 0.0083 | 0.9984 | 0.9984 | 0.9984 | 0.9983 |
| 0.0232 | 1.21 | 230 | 0.0057 | 0.9984 | 0.9984 | 0.9984 | 0.9984 |
| 0.0035 | 1.26 | 240 | 0.0056 | 0.9987 | 0.9987 | 0.9987 | 0.9987 |
| 0.0246 | 1.32 | 250 | 0.0053 | 0.9984 | 0.9984 | 0.9984 | 0.9983 |
| 0.0023 | 1.37 | 260 | 0.0063 | 0.9980 | 0.9980 | 0.9981 | 0.9980 |
| 0.0021 | 1.42 | 270 | 0.0048 | 0.9984 | 0.9984 | 0.9984 | 0.9983 |
| 0.002 | 1.47 | 280 | 0.0028 | 0.9997 | 0.9997 | 0.9997 | 0.9997 |
| 0.022 | 1.53 | 290 | 0.0023 | 0.9997 | 0.9997 | 0.9997 | 0.9997 |
| 0.0135 | 1.58 | 300 | 0.0046 | 0.9987 | 0.9987 | 0.9987 | 0.9987 |
| 0.0026 | 1.63 | 310 | 0.0082 | 0.9977 | 0.9977 | 0.9979 | 0.9976 |
| 0.0019 | 1.68 | 320 | 0.0043 | 0.9990 | 0.9990 | 0.9991 | 0.9990 |
| 0.0017 | 1.74 | 330 | 0.0035 | 0.9993 | 0.9994 | 0.9994 | 0.9994 |
| 0.0019 | 1.79 | 340 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0014 | 1.84 | 350 | 0.0013 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0014 | 1.89 | 360 | 0.0013 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 1.95 | 370 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 2.0 | 380 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0012 | 2.05 | 390 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 2.11 | 400 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 2.16 | 410 | 0.0010 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 2.21 | 420 | 0.0010 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0014 | 2.26 | 430 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.001 | 2.32 | 440 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.001 | 2.37 | 450 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 2.42 | 460 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 2.47 | 470 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 2.53 | 480 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 2.58 | 490 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 2.63 | 500 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 2.68 | 510 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 2.74 | 520 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 2.79 | 530 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 2.84 | 540 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 2.89 | 550 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 2.95 | 560 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 3.0 | 570 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 3.05 | 580 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 3.11 | 590 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 3.16 | 600 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 3.21 | 610 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 3.26 | 620 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 3.32 | 630 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 3.37 | 640 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 3.42 | 650 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 3.47 | 660 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 3.53 | 670 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 3.58 | 680 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 3.63 | 690 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 3.68 | 700 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 3.74 | 710 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 3.79 | 720 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 3.84 | 730 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 3.89 | 740 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 3.95 | 750 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.0 | 760 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.05 | 770 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.11 | 780 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.16 | 790 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.21 | 800 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.26 | 810 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.32 | 820 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.37 | 830 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.42 | 840 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.47 | 850 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.53 | 860 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.58 | 870 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 4.63 | 880 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.68 | 890 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 4.74 | 900 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 4.79 | 910 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.84 | 920 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 4.89 | 930 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 4.95 | 940 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 5.0 | 950 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
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