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---
library_name: transformers
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: 100epoch_test
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. -->
# 100epoch_test
This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3027
- Precision: 0.9074
- Recall: 0.9128
- F1: 0.9101
- Accuracy: 0.9717
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.7937 | 100 | 0.1798 | 0.8560 | 0.8877 | 0.8716 | 0.9565 |
| No log | 1.5873 | 200 | 0.1305 | 0.8982 | 0.9032 | 0.9007 | 0.9679 |
| No log | 2.3810 | 300 | 0.1216 | 0.9019 | 0.9290 | 0.9152 | 0.9719 |
| No log | 3.1746 | 400 | 0.1334 | 0.8883 | 0.9062 | 0.8971 | 0.9676 |
| 0.2125 | 3.9683 | 500 | 0.1163 | 0.9103 | 0.9191 | 0.9147 | 0.9726 |
| 0.2125 | 4.7619 | 600 | 0.1171 | 0.9172 | 0.9002 | 0.9086 | 0.9712 |
| 0.2125 | 5.5556 | 700 | 0.1343 | 0.8646 | 0.9006 | 0.8822 | 0.9635 |
| 0.2125 | 6.3492 | 800 | 0.1397 | 0.9085 | 0.8956 | 0.9020 | 0.9690 |
| 0.2125 | 7.1429 | 900 | 0.1437 | 0.9166 | 0.8969 | 0.9067 | 0.9704 |
| 0.0606 | 7.9365 | 1000 | 0.1241 | 0.9075 | 0.9138 | 0.9106 | 0.9716 |
| 0.0606 | 8.7302 | 1100 | 0.1372 | 0.9112 | 0.9088 | 0.9100 | 0.9710 |
| 0.0606 | 9.5238 | 1200 | 0.1403 | 0.9151 | 0.9154 | 0.9153 | 0.9735 |
| 0.0606 | 10.3175 | 1300 | 0.1783 | 0.9169 | 0.9039 | 0.9103 | 0.9720 |
| 0.0606 | 11.1111 | 1400 | 0.1517 | 0.9148 | 0.9151 | 0.9149 | 0.9735 |
| 0.0374 | 11.9048 | 1500 | 0.1677 | 0.9148 | 0.9042 | 0.9095 | 0.9712 |
| 0.0374 | 12.6984 | 1600 | 0.1679 | 0.9178 | 0.9006 | 0.9091 | 0.9711 |
| 0.0374 | 13.4921 | 1700 | 0.1569 | 0.9178 | 0.8996 | 0.9086 | 0.9712 |
| 0.0374 | 14.2857 | 1800 | 0.2076 | 0.9066 | 0.8916 | 0.8991 | 0.9677 |
| 0.0374 | 15.0794 | 1900 | 0.1684 | 0.9001 | 0.9201 | 0.9100 | 0.9712 |
| 0.0231 | 15.8730 | 2000 | 0.1945 | 0.9163 | 0.9108 | 0.9135 | 0.9725 |
| 0.0231 | 16.6667 | 2100 | 0.1707 | 0.9099 | 0.9072 | 0.9085 | 0.9715 |
| 0.0231 | 17.4603 | 2200 | 0.2078 | 0.9167 | 0.9019 | 0.9092 | 0.9715 |
| 0.0231 | 18.2540 | 2300 | 0.2086 | 0.9068 | 0.9032 | 0.9050 | 0.9700 |
| 0.0231 | 19.0476 | 2400 | 0.2082 | 0.9110 | 0.8996 | 0.9053 | 0.9705 |
| 0.0155 | 19.8413 | 2500 | 0.1908 | 0.9109 | 0.9121 | 0.9115 | 0.9719 |
| 0.0155 | 20.6349 | 2600 | 0.1991 | 0.9094 | 0.9118 | 0.9106 | 0.9712 |
| 0.0155 | 21.4286 | 2700 | 0.2008 | 0.9053 | 0.9065 | 0.9059 | 0.9702 |
| 0.0155 | 22.2222 | 2800 | 0.2344 | 0.9097 | 0.9125 | 0.9111 | 0.9720 |
| 0.0155 | 23.0159 | 2900 | 0.2186 | 0.9076 | 0.9115 | 0.9095 | 0.9717 |
| 0.0097 | 23.8095 | 3000 | 0.2208 | 0.9045 | 0.9141 | 0.9093 | 0.9716 |
| 0.0097 | 24.6032 | 3100 | 0.1988 | 0.9033 | 0.9039 | 0.9036 | 0.9700 |
| 0.0097 | 25.3968 | 3200 | 0.2291 | 0.9197 | 0.9009 | 0.9102 | 0.9712 |
| 0.0097 | 26.1905 | 3300 | 0.2402 | 0.9011 | 0.9025 | 0.9018 | 0.9697 |
| 0.0097 | 26.9841 | 3400 | 0.2418 | 0.9095 | 0.9131 | 0.9113 | 0.9715 |
| 0.0066 | 27.7778 | 3500 | 0.2149 | 0.8997 | 0.9065 | 0.9031 | 0.9697 |
| 0.0066 | 28.5714 | 3600 | 0.2474 | 0.9016 | 0.9022 | 0.9019 | 0.9696 |
| 0.0066 | 29.3651 | 3700 | 0.2362 | 0.9143 | 0.9055 | 0.9099 | 0.9720 |
| 0.0066 | 30.1587 | 3800 | 0.2374 | 0.9058 | 0.9184 | 0.9121 | 0.9719 |
| 0.0066 | 30.9524 | 3900 | 0.2516 | 0.9032 | 0.9006 | 0.9019 | 0.9692 |
| 0.0048 | 31.7460 | 4000 | 0.2251 | 0.9038 | 0.9092 | 0.9065 | 0.9710 |
| 0.0048 | 32.5397 | 4100 | 0.2488 | 0.9062 | 0.9098 | 0.9080 | 0.9709 |
| 0.0048 | 33.3333 | 4200 | 0.2412 | 0.9034 | 0.9088 | 0.9061 | 0.9710 |
| 0.0048 | 34.1270 | 4300 | 0.2421 | 0.9041 | 0.9032 | 0.9037 | 0.9697 |
| 0.0048 | 34.9206 | 4400 | 0.2599 | 0.9038 | 0.9062 | 0.9050 | 0.9704 |
| 0.0042 | 35.7143 | 4500 | 0.2372 | 0.9002 | 0.9144 | 0.9072 | 0.9706 |
| 0.0042 | 36.5079 | 4600 | 0.2545 | 0.9020 | 0.9035 | 0.9028 | 0.9696 |
| 0.0042 | 37.3016 | 4700 | 0.2629 | 0.9034 | 0.8963 | 0.8998 | 0.9684 |
| 0.0042 | 38.0952 | 4800 | 0.2407 | 0.9069 | 0.9078 | 0.9074 | 0.9706 |
| 0.0042 | 38.8889 | 4900 | 0.2604 | 0.9115 | 0.9019 | 0.9067 | 0.9706 |
| 0.0039 | 39.6825 | 5000 | 0.2657 | 0.9091 | 0.9052 | 0.9071 | 0.9704 |
| 0.0039 | 40.4762 | 5100 | 0.2615 | 0.8995 | 0.9049 | 0.9022 | 0.9690 |
| 0.0039 | 41.2698 | 5200 | 0.2631 | 0.9115 | 0.9016 | 0.9065 | 0.9711 |
| 0.0039 | 42.0635 | 5300 | 0.2645 | 0.9087 | 0.9105 | 0.9096 | 0.9717 |
| 0.0039 | 42.8571 | 5400 | 0.2736 | 0.9027 | 0.9042 | 0.9034 | 0.9701 |
| 0.0031 | 43.6508 | 5500 | 0.2491 | 0.9064 | 0.9052 | 0.9058 | 0.9702 |
| 0.0031 | 44.4444 | 5600 | 0.2556 | 0.9110 | 0.9095 | 0.9102 | 0.9712 |
| 0.0031 | 45.2381 | 5700 | 0.2768 | 0.9009 | 0.9009 | 0.9009 | 0.9689 |
| 0.0031 | 46.0317 | 5800 | 0.2580 | 0.9054 | 0.9045 | 0.9050 | 0.9702 |
| 0.0031 | 46.8254 | 5900 | 0.2524 | 0.9047 | 0.9068 | 0.9058 | 0.9709 |
| 0.003 | 47.6190 | 6000 | 0.2652 | 0.9083 | 0.9101 | 0.9092 | 0.9717 |
| 0.003 | 48.4127 | 6100 | 0.2741 | 0.9082 | 0.9148 | 0.9115 | 0.9717 |
| 0.003 | 49.2063 | 6200 | 0.2835 | 0.9065 | 0.9062 | 0.9063 | 0.9706 |
| 0.003 | 50.0 | 6300 | 0.2906 | 0.9011 | 0.9058 | 0.9035 | 0.9699 |
| 0.003 | 50.7937 | 6400 | 0.2738 | 0.9060 | 0.9101 | 0.9080 | 0.9709 |
| 0.0019 | 51.5873 | 6500 | 0.2730 | 0.9088 | 0.9085 | 0.9086 | 0.9715 |
| 0.0019 | 52.3810 | 6600 | 0.2718 | 0.9042 | 0.9012 | 0.9027 | 0.9696 |
| 0.0019 | 53.1746 | 6700 | 0.2862 | 0.9101 | 0.9101 | 0.9101 | 0.9712 |
| 0.0019 | 53.9683 | 6800 | 0.2816 | 0.8985 | 0.9095 | 0.9040 | 0.9695 |
| 0.0019 | 54.7619 | 6900 | 0.2931 | 0.9051 | 0.9016 | 0.9033 | 0.9697 |
| 0.0017 | 55.5556 | 7000 | 0.2644 | 0.9082 | 0.9082 | 0.9082 | 0.9710 |
| 0.0017 | 56.3492 | 7100 | 0.2815 | 0.9089 | 0.9068 | 0.9079 | 0.9715 |
| 0.0017 | 57.1429 | 7200 | 0.2566 | 0.9044 | 0.9068 | 0.9056 | 0.9709 |
| 0.0017 | 57.9365 | 7300 | 0.2709 | 0.9130 | 0.9088 | 0.9109 | 0.9722 |
| 0.0017 | 58.7302 | 7400 | 0.2699 | 0.9089 | 0.9092 | 0.9090 | 0.9715 |
| 0.0016 | 59.5238 | 7500 | 0.2742 | 0.9084 | 0.9072 | 0.9078 | 0.9707 |
| 0.0016 | 60.3175 | 7600 | 0.2549 | 0.9062 | 0.9101 | 0.9082 | 0.9714 |
| 0.0016 | 61.1111 | 7700 | 0.2714 | 0.9068 | 0.8963 | 0.9015 | 0.9695 |
| 0.0016 | 61.9048 | 7800 | 0.2801 | 0.9098 | 0.9062 | 0.9080 | 0.9715 |
| 0.0016 | 62.6984 | 7900 | 0.2818 | 0.9006 | 0.9072 | 0.9039 | 0.9705 |
| 0.0013 | 63.4921 | 8000 | 0.2923 | 0.9053 | 0.9068 | 0.9061 | 0.9711 |
| 0.0013 | 64.2857 | 8100 | 0.2944 | 0.9068 | 0.8900 | 0.8983 | 0.9686 |
| 0.0013 | 65.0794 | 8200 | 0.2941 | 0.9033 | 0.9075 | 0.9054 | 0.9706 |
| 0.0013 | 65.8730 | 8300 | 0.2801 | 0.9075 | 0.9006 | 0.9040 | 0.9699 |
| 0.0013 | 66.6667 | 8400 | 0.2822 | 0.9098 | 0.9131 | 0.9115 | 0.9722 |
| 0.0008 | 67.4603 | 8500 | 0.3013 | 0.9066 | 0.9016 | 0.9041 | 0.9701 |
| 0.0008 | 68.2540 | 8600 | 0.2670 | 0.9040 | 0.9144 | 0.9092 | 0.9719 |
| 0.0008 | 69.0476 | 8700 | 0.2941 | 0.9054 | 0.9012 | 0.9033 | 0.9701 |
| 0.0008 | 69.8413 | 8800 | 0.2911 | 0.9086 | 0.9065 | 0.9076 | 0.9717 |
| 0.0008 | 70.6349 | 8900 | 0.2783 | 0.9123 | 0.9111 | 0.9117 | 0.9726 |
| 0.0007 | 71.4286 | 9000 | 0.2877 | 0.9122 | 0.9022 | 0.9072 | 0.9715 |
| 0.0007 | 72.2222 | 9100 | 0.3021 | 0.9019 | 0.9138 | 0.9078 | 0.9706 |
| 0.0007 | 73.0159 | 9200 | 0.2869 | 0.9094 | 0.9118 | 0.9106 | 0.9721 |
| 0.0007 | 73.8095 | 9300 | 0.2928 | 0.9041 | 0.9095 | 0.9068 | 0.9706 |
| 0.0007 | 74.6032 | 9400 | 0.2896 | 0.9088 | 0.9088 | 0.9088 | 0.9716 |
| 0.0007 | 75.3968 | 9500 | 0.3008 | 0.9073 | 0.9118 | 0.9095 | 0.9716 |
| 0.0007 | 76.1905 | 9600 | 0.3019 | 0.9067 | 0.9082 | 0.9074 | 0.9710 |
| 0.0007 | 76.9841 | 9700 | 0.2924 | 0.9073 | 0.9148 | 0.9110 | 0.9717 |
| 0.0007 | 77.7778 | 9800 | 0.2856 | 0.9117 | 0.9138 | 0.9127 | 0.9726 |
| 0.0007 | 78.5714 | 9900 | 0.2924 | 0.9098 | 0.9098 | 0.9098 | 0.9719 |
| 0.0004 | 79.3651 | 10000 | 0.3100 | 0.9047 | 0.9121 | 0.9084 | 0.9715 |
| 0.0004 | 80.1587 | 10100 | 0.3055 | 0.9100 | 0.9082 | 0.9091 | 0.9719 |
| 0.0004 | 80.9524 | 10200 | 0.2990 | 0.9103 | 0.9125 | 0.9114 | 0.9725 |
| 0.0004 | 81.7460 | 10300 | 0.2980 | 0.9099 | 0.9039 | 0.9069 | 0.9714 |
| 0.0004 | 82.5397 | 10400 | 0.2954 | 0.9095 | 0.9128 | 0.9111 | 0.9724 |
| 0.0003 | 83.3333 | 10500 | 0.2993 | 0.9092 | 0.9125 | 0.9108 | 0.9722 |
| 0.0003 | 84.1270 | 10600 | 0.3036 | 0.9079 | 0.9118 | 0.9098 | 0.9720 |
| 0.0003 | 84.9206 | 10700 | 0.2919 | 0.9088 | 0.9121 | 0.9105 | 0.9722 |
| 0.0003 | 85.7143 | 10800 | 0.2948 | 0.9075 | 0.9105 | 0.9090 | 0.9719 |
| 0.0003 | 86.5079 | 10900 | 0.3037 | 0.9081 | 0.9078 | 0.9080 | 0.9717 |
| 0.0003 | 87.3016 | 11000 | 0.3039 | 0.9086 | 0.9125 | 0.9105 | 0.9724 |
| 0.0003 | 88.0952 | 11100 | 0.3019 | 0.9088 | 0.9115 | 0.9101 | 0.9721 |
| 0.0003 | 88.8889 | 11200 | 0.3064 | 0.9108 | 0.9111 | 0.9110 | 0.9724 |
| 0.0003 | 89.6825 | 11300 | 0.3017 | 0.9088 | 0.9115 | 0.9101 | 0.9721 |
| 0.0003 | 90.4762 | 11400 | 0.2813 | 0.9074 | 0.9098 | 0.9086 | 0.9717 |
| 0.0005 | 91.2698 | 11500 | 0.2895 | 0.9081 | 0.9078 | 0.9080 | 0.9716 |
| 0.0005 | 92.0635 | 11600 | 0.2950 | 0.9065 | 0.9098 | 0.9082 | 0.9716 |
| 0.0005 | 92.8571 | 11700 | 0.2880 | 0.9074 | 0.9134 | 0.9104 | 0.9720 |
| 0.0005 | 93.6508 | 11800 | 0.2947 | 0.9066 | 0.9134 | 0.9100 | 0.9717 |
| 0.0005 | 94.4444 | 11900 | 0.3003 | 0.9050 | 0.9068 | 0.9059 | 0.9706 |
| 0.0002 | 95.2381 | 12000 | 0.3018 | 0.9064 | 0.9115 | 0.9089 | 0.9714 |
| 0.0002 | 96.0317 | 12100 | 0.3008 | 0.9071 | 0.9131 | 0.9101 | 0.9717 |
| 0.0002 | 96.8254 | 12200 | 0.3011 | 0.9071 | 0.9131 | 0.9101 | 0.9717 |
| 0.0002 | 97.6190 | 12300 | 0.3007 | 0.9077 | 0.9134 | 0.9106 | 0.9719 |
| 0.0002 | 98.4127 | 12400 | 0.3017 | 0.9077 | 0.9134 | 0.9106 | 0.9719 |
| 0.0002 | 99.2063 | 12500 | 0.3024 | 0.9074 | 0.9128 | 0.9101 | 0.9717 |
| 0.0002 | 100.0 | 12600 | 0.3027 | 0.9074 | 0.9128 | 0.9101 | 0.9717 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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