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--- |
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base_model: uer/gpt2-chinese-cluecorpussmall |
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tags: |
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- generated_from_trainer |
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datasets: |
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- lip_service4chan |
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model-index: |
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- name: lib_service_4chan |
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results: [] |
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language: |
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- zh |
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pipeline_tag: conversational |
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--- |
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# lib_service_4chan |
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This model is a fine-tuned version of [uer/gpt2-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-chinese-cluecorpussmall) on the [lip_service_4chan](https://huggingface.co/datasets/qgyd2021/lip_service_4chan) dataset. |
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Lip Service 满嘴芬芳，吵架陪练员。 |
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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: 0.0002 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 16 |
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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: 1.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 2.716 | 0.01 | 100 | 1.9495 | |
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| 1.8985 | 0.02 | 200 | 1.6915 | |
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| 1.7151 | 0.02 | 300 | 1.5763 | |
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| 1.6217 | 0.03 | 400 | 1.5115 | |
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| 1.564 | 0.04 | 500 | 1.4694 | |
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| 1.5461 | 0.05 | 600 | 1.4379 | |
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| 1.4943 | 0.06 | 700 | 1.4127 | |
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| 1.4737 | 0.07 | 800 | 1.3890 | |
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| 1.4399 | 0.07 | 900 | 1.3813 | |
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| 1.4356 | 0.08 | 1000 | 1.3540 | |
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| 1.3999 | 0.09 | 1100 | 1.3329 | |
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| 1.3668 | 0.1 | 1200 | 1.3153 | |
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| 1.3604 | 0.11 | 1300 | 1.3029 | |
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| 1.3352 | 0.12 | 1400 | 1.2834 | |
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| 1.3278 | 0.12 | 1500 | 1.2619 | |
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| 1.315 | 0.13 | 1600 | 1.2539 | |
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| 1.2854 | 0.14 | 1700 | 1.2432 | |
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| 1.292 | 0.15 | 1800 | 1.2288 | |
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| 1.2795 | 0.16 | 1900 | 1.2188 | |
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| 1.2677 | 0.16 | 2000 | 1.2059 | |
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| 1.2599 | 0.17 | 2100 | 1.2019 | |
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| 1.2479 | 0.18 | 2200 | 1.1915 | |
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| 1.2245 | 0.19 | 2300 | 1.1827 | |
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| 1.2326 | 0.2 | 2400 | 1.1734 | |
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| 1.2124 | 0.21 | 2500 | 1.1660 | |
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| 1.2171 | 0.21 | 2600 | 1.1576 | |
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| 1.1917 | 0.22 | 2700 | 1.1518 | |
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| 1.1867 | 0.23 | 2800 | 1.1444 | |
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| 1.1821 | 0.24 | 2900 | 1.1386 | |
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| 1.1741 | 0.25 | 3000 | 1.1347 | |
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| 1.1753 | 0.25 | 3100 | 1.1293 | |
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| 1.1629 | 0.26 | 3200 | 1.1264 | |
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| 1.1694 | 0.27 | 3300 | 1.1201 | |
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| 1.1482 | 0.28 | 3400 | 1.1146 | |
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| 1.156 | 0.29 | 3500 | 1.1052 | |
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| 1.1512 | 0.3 | 3600 | 1.0982 | |
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| 1.142 | 0.3 | 3700 | 1.0971 | |
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| 1.1544 | 0.31 | 3800 | 1.0920 | |
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| 1.1312 | 0.32 | 3900 | 1.0869 | |
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| 1.1394 | 0.33 | 4000 | 1.0808 | |
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| 1.123 | 0.34 | 4100 | 1.0747 | |
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| 1.1154 | 0.35 | 4200 | 1.0715 | |
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| 1.1064 | 0.35 | 4300 | 1.0674 | |
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| 1.1245 | 0.36 | 4400 | 1.0620 | |
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| 1.1036 | 0.37 | 4500 | 1.0575 | |
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| 1.0963 | 0.38 | 4600 | 1.0568 | |
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| 1.0987 | 0.39 | 4700 | 1.0491 | |
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| 1.0859 | 0.39 | 4800 | 1.0443 | |
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| 1.0845 | 0.4 | 4900 | 1.0432 | |
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| 1.0938 | 0.41 | 5000 | 1.0410 | |
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| 1.087 | 0.42 | 5100 | 1.0334 | |
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| 1.077 | 0.43 | 5200 | 1.0324 | |
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| 1.0787 | 0.44 | 5300 | 1.0276 | |
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| 1.068 | 0.44 | 5400 | 1.0220 | |
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| 1.0748 | 0.45 | 5500 | 1.0199 | |
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| 1.0622 | 0.46 | 5600 | 1.0169 | |
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| 1.0555 | 0.47 | 5700 | 1.0153 | |
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| 1.0498 | 0.48 | 5800 | 1.0100 | |
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| 1.055 | 0.49 | 5900 | 1.0074 | |
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| 1.0424 | 0.49 | 6000 | 1.0020 | |
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| 1.0465 | 0.5 | 6100 | 0.9976 | |
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| 1.0414 | 0.51 | 6200 | 0.9942 | |
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| 1.0355 | 0.52 | 6300 | 0.9919 | |
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| 1.0234 | 0.53 | 6400 | 0.9883 | |
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| 1.0205 | 0.53 | 6500 | 0.9857 | |
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| 1.0316 | 0.54 | 6600 | 0.9805 | |
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| 1.0137 | 0.55 | 6700 | 0.9788 | |
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| 1.0222 | 0.56 | 6800 | 0.9773 | |
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| 1.0219 | 0.57 | 6900 | 0.9722 | |
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| 1.0032 | 0.58 | 7000 | 0.9706 | |
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| 1.0039 | 0.58 | 7100 | 0.9669 | |
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| 1.0166 | 0.59 | 7200 | 0.9635 | |
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| 1.0065 | 0.6 | 7300 | 0.9614 | |
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| 1.0087 | 0.61 | 7400 | 0.9574 | |
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| 0.9968 | 0.62 | 7500 | 0.9525 | |
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| 1.0031 | 0.62 | 7600 | 0.9503 | |
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| 0.99 | 0.63 | 7700 | 0.9491 | |
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| 0.9946 | 0.64 | 7800 | 0.9457 | |
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| 0.9944 | 0.65 | 7900 | 0.9424 | |
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| 0.9854 | 0.66 | 8000 | 0.9399 | |
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| 0.9797 | 0.67 | 8100 | 0.9364 | |
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| 0.9804 | 0.67 | 8200 | 0.9341 | |
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| 0.9835 | 0.68 | 8300 | 0.9318 | |
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| 0.9849 | 0.69 | 8400 | 0.9299 | |
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| 0.9753 | 0.7 | 8500 | 0.9274 | |
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| 0.975 | 0.71 | 8600 | 0.9238 | |
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| 0.9649 | 0.72 | 8700 | 0.9225 | |
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| 0.9654 | 0.72 | 8800 | 0.9202 | |
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| 0.958 | 0.73 | 8900 | 0.9167 | |
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| 0.9679 | 0.74 | 9000 | 0.9143 | |
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| 0.9631 | 0.75 | 9100 | 0.9110 | |
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| 0.9633 | 0.76 | 9200 | 0.9086 | |
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| 0.9495 | 0.76 | 9300 | 0.9071 | |
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| 0.9625 | 0.77 | 9400 | 0.9036 | |
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| 0.9519 | 0.78 | 9500 | 0.9023 | |
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| 0.9399 | 0.79 | 9600 | 0.8993 | |
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| 0.9624 | 0.8 | 9700 | 0.8973 | |
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| 0.9418 | 0.81 | 9800 | 0.8963 | |
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| 0.9394 | 0.81 | 9900 | 0.8933 | |
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| 0.947 | 0.82 | 10000 | 0.8919 | |
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| 0.9326 | 0.83 | 10100 | 0.8900 | |
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| 0.9326 | 0.84 | 10200 | 0.8886 | |
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| 0.9343 | 0.85 | 10300 | 0.8860 | |
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| 0.9263 | 0.85 | 10400 | 0.8841 | |
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| 0.9256 | 0.86 | 10500 | 0.8818 | |
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| 0.9373 | 0.87 | 10600 | 0.8807 | |
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| 0.9314 | 0.88 | 10700 | 0.8789 | |
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| 0.9203 | 0.89 | 10800 | 0.8770 | |
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| 0.927 | 0.9 | 10900 | 0.8754 | |
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| 0.934 | 0.9 | 11000 | 0.8744 | |
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| 0.9193 | 0.91 | 11100 | 0.8727 | |
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| 0.9185 | 0.92 | 11200 | 0.8714 | |
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| 0.9188 | 0.93 | 11300 | 0.8702 | |
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| 0.9165 | 0.94 | 11400 | 0.8693 | |
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| 0.9209 | 0.95 | 11500 | 0.8682 | |
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| 0.9241 | 0.95 | 11600 | 0.8670 | |
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| 0.9182 | 0.96 | 11700 | 0.8662 | |
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| 0.9076 | 0.97 | 11800 | 0.8653 | |
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| 0.9225 | 0.98 | 11900 | 0.8643 | |
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| 0.9094 | 0.99 | 12000 | 0.8640 | |
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| 0.913 | 0.99 | 12100 | 0.8635 | |
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### Framework versions |
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- Transformers 4.33.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
