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---
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license:
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base_model:
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name:
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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#
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This model is a fine-tuned version of [
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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### Framework versions
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---
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license: mit
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base_model: roberta-large
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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+
- name: roberta-large-sst-2-16-13
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results: []
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| 11 |
---
|
| 12 |
|
| 13 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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| 14 |
should probably proofread and complete it, then remove this comment. -->
|
| 15 |
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| 16 |
+
# roberta-large-sst-2-16-13
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3222
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- Accuracy: 0.8438
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| No log | 1.0 | 1 | 0.7045 | 0.5 |
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| No log | 2.0 | 2 | 0.7045 | 0.5 |
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| No log | 3.0 | 3 | 0.7045 | 0.5 |
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| No log | 4.0 | 4 | 0.7045 | 0.5 |
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| No log | 5.0 | 5 | 0.7045 | 0.5 |
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| No log | 6.0 | 6 | 0.7045 | 0.5 |
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| No log | 7.0 | 7 | 0.7044 | 0.5 |
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| No log | 8.0 | 8 | 0.7044 | 0.5 |
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| No log | 9.0 | 9 | 0.7044 | 0.5 |
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| 0.7125 | 10.0 | 10 | 0.7043 | 0.5 |
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| 0.7125 | 11.0 | 11 | 0.7043 | 0.5 |
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| 0.7125 | 12.0 | 12 | 0.7042 | 0.5 |
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| 0.7125 | 13.0 | 13 | 0.7042 | 0.5 |
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| 0.7125 | 14.0 | 14 | 0.7041 | 0.5 |
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| 0.7125 | 15.0 | 15 | 0.7041 | 0.5 |
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| 0.7125 | 16.0 | 16 | 0.7040 | 0.5 |
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| 0.7125 | 17.0 | 17 | 0.7040 | 0.5 |
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| 0.7125 | 18.0 | 18 | 0.7039 | 0.5 |
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| 0.7125 | 19.0 | 19 | 0.7039 | 0.5 |
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| 0.6935 | 20.0 | 20 | 0.7038 | 0.5 |
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| 0.6935 | 21.0 | 21 | 0.7038 | 0.5 |
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| 0.6935 | 22.0 | 22 | 0.7037 | 0.5 |
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| 0.6935 | 23.0 | 23 | 0.7037 | 0.5 |
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| 0.6935 | 24.0 | 24 | 0.7037 | 0.5 |
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| 0.6935 | 25.0 | 25 | 0.7036 | 0.5 |
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| 78 |
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| 0.6935 | 26.0 | 26 | 0.7036 | 0.5 |
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| 0.6935 | 27.0 | 27 | 0.7035 | 0.5 |
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| 0.6935 | 28.0 | 28 | 0.7035 | 0.5 |
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| 81 |
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| 0.6935 | 29.0 | 29 | 0.7034 | 0.5 |
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| 0.7031 | 30.0 | 30 | 0.7033 | 0.5 |
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| 0.7031 | 31.0 | 31 | 0.7032 | 0.5 |
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| 84 |
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| 0.7031 | 32.0 | 32 | 0.7031 | 0.5 |
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| 0.7031 | 33.0 | 33 | 0.7030 | 0.5 |
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| 0.7031 | 34.0 | 34 | 0.7029 | 0.5 |
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| 0.7031 | 35.0 | 35 | 0.7027 | 0.5 |
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| 0.7031 | 36.0 | 36 | 0.7027 | 0.5 |
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| 0.7031 | 37.0 | 37 | 0.7026 | 0.5 |
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| 0.7031 | 38.0 | 38 | 0.7025 | 0.5 |
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| 91 |
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| 0.7031 | 39.0 | 39 | 0.7024 | 0.5 |
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| 0.7021 | 40.0 | 40 | 0.7023 | 0.5 |
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| 0.7021 | 41.0 | 41 | 0.7022 | 0.5 |
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| 0.7021 | 42.0 | 42 | 0.7021 | 0.5 |
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| 0.7021 | 43.0 | 43 | 0.7019 | 0.5 |
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| 0.7021 | 44.0 | 44 | 0.7017 | 0.5 |
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| 0.7021 | 45.0 | 45 | 0.7016 | 0.5 |
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| 0.7021 | 46.0 | 46 | 0.7014 | 0.5 |
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| 0.7021 | 47.0 | 47 | 0.7012 | 0.5 |
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| 0.7021 | 48.0 | 48 | 0.7010 | 0.5 |
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| 0.7021 | 49.0 | 49 | 0.7007 | 0.5 |
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| 0.7009 | 50.0 | 50 | 0.7005 | 0.5 |
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| 0.7009 | 51.0 | 51 | 0.7003 | 0.5 |
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| 0.7009 | 52.0 | 52 | 0.7001 | 0.5 |
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| 0.7009 | 53.0 | 53 | 0.6998 | 0.5 |
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| 0.7009 | 54.0 | 54 | 0.6996 | 0.5 |
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| 0.7009 | 55.0 | 55 | 0.6994 | 0.5 |
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| 0.7009 | 56.0 | 56 | 0.6993 | 0.5 |
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| 0.7009 | 57.0 | 57 | 0.6992 | 0.5 |
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| 0.7009 | 58.0 | 58 | 0.6990 | 0.5 |
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| 0.7009 | 59.0 | 59 | 0.6988 | 0.5 |
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| 0.6866 | 60.0 | 60 | 0.6986 | 0.5 |
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| 0.6866 | 61.0 | 61 | 0.6984 | 0.5 |
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| 0.6866 | 62.0 | 62 | 0.6983 | 0.5 |
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| 0.6866 | 63.0 | 63 | 0.6981 | 0.5 |
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| 0.6866 | 64.0 | 64 | 0.6979 | 0.5 |
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| 0.6866 | 65.0 | 65 | 0.6977 | 0.5 |
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| 0.6866 | 66.0 | 66 | 0.6976 | 0.4688 |
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| 0.6866 | 67.0 | 67 | 0.6974 | 0.4688 |
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| 0.6866 | 68.0 | 68 | 0.6972 | 0.4688 |
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| 0.6866 | 69.0 | 69 | 0.6970 | 0.4688 |
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| 122 |
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| 0.6818 | 70.0 | 70 | 0.6968 | 0.4688 |
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| 0.6818 | 71.0 | 71 | 0.6966 | 0.4688 |
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| 0.6818 | 72.0 | 72 | 0.6964 | 0.4688 |
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| 0.6818 | 73.0 | 73 | 0.6961 | 0.4688 |
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| 0.6818 | 74.0 | 74 | 0.6960 | 0.4688 |
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| 0.6818 | 75.0 | 75 | 0.6959 | 0.4688 |
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| 128 |
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| 0.6818 | 76.0 | 76 | 0.6957 | 0.4688 |
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| 0.6818 | 77.0 | 77 | 0.6955 | 0.4688 |
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| 0.6818 | 78.0 | 78 | 0.6953 | 0.4688 |
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| 0.6818 | 79.0 | 79 | 0.6948 | 0.4688 |
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| 132 |
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| 0.6639 | 80.0 | 80 | 0.6940 | 0.4688 |
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| 133 |
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| 0.6639 | 81.0 | 81 | 0.6932 | 0.4688 |
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| 0.6639 | 82.0 | 82 | 0.6925 | 0.4688 |
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| 135 |
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| 0.6639 | 83.0 | 83 | 0.6916 | 0.4688 |
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| 136 |
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| 0.6639 | 84.0 | 84 | 0.6908 | 0.5 |
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| 137 |
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| 0.6639 | 85.0 | 85 | 0.6899 | 0.5 |
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| 138 |
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| 0.6639 | 86.0 | 86 | 0.6889 | 0.5 |
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| 139 |
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| 0.6639 | 87.0 | 87 | 0.6878 | 0.5 |
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| 140 |
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| 0.6639 | 88.0 | 88 | 0.6869 | 0.5 |
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| 141 |
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| 0.6639 | 89.0 | 89 | 0.6859 | 0.4688 |
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| 142 |
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| 0.6652 | 90.0 | 90 | 0.6850 | 0.4688 |
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| 143 |
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| 0.6652 | 91.0 | 91 | 0.6842 | 0.4688 |
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| 144 |
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| 0.6652 | 92.0 | 92 | 0.6836 | 0.5312 |
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| 145 |
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| 0.6652 | 93.0 | 93 | 0.6829 | 0.5312 |
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| 146 |
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| 0.6652 | 94.0 | 94 | 0.6818 | 0.5625 |
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| 147 |
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| 0.6652 | 95.0 | 95 | 0.6806 | 0.5938 |
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| 148 |
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| 0.6652 | 96.0 | 96 | 0.6792 | 0.5938 |
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| 149 |
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| 0.6652 | 97.0 | 97 | 0.6783 | 0.5938 |
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| 0.6652 | 98.0 | 98 | 0.6771 | 0.5938 |
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| 0.6652 | 99.0 | 99 | 0.6758 | 0.5938 |
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| 152 |
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| 0.621 | 100.0 | 100 | 0.6743 | 0.5938 |
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| 0.621 | 101.0 | 101 | 0.6725 | 0.5938 |
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| 0.621 | 102.0 | 102 | 0.6711 | 0.5938 |
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| 0.621 | 103.0 | 103 | 0.6708 | 0.5938 |
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| 0.621 | 104.0 | 104 | 0.6713 | 0.625 |
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| 157 |
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| 0.621 | 105.0 | 105 | 0.6693 | 0.5938 |
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| 0.621 | 106.0 | 106 | 0.6605 | 0.5938 |
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| 159 |
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| 0.621 | 107.0 | 107 | 0.6499 | 0.5938 |
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| 0.621 | 108.0 | 108 | 0.6439 | 0.5625 |
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| 161 |
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| 0.621 | 109.0 | 109 | 0.6434 | 0.625 |
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| 0.5331 | 110.0 | 110 | 0.6439 | 0.5938 |
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| 0.5331 | 111.0 | 111 | 0.6418 | 0.5625 |
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| 164 |
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| 0.5331 | 112.0 | 112 | 0.6388 | 0.5625 |
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| 0.5331 | 113.0 | 113 | 0.6346 | 0.5625 |
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| 0.5331 | 114.0 | 114 | 0.6307 | 0.5625 |
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| 167 |
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| 0.5331 | 115.0 | 115 | 0.6275 | 0.5625 |
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| 168 |
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| 0.5331 | 116.0 | 116 | 0.6230 | 0.5625 |
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| 169 |
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| 0.5331 | 117.0 | 117 | 0.6144 | 0.5625 |
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| 170 |
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| 0.5331 | 118.0 | 118 | 0.6092 | 0.5625 |
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| 171 |
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| 0.5331 | 119.0 | 119 | 0.6042 | 0.5938 |
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| 172 |
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| 0.4594 | 120.0 | 120 | 0.6006 | 0.5938 |
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| 0.4594 | 121.0 | 121 | 0.5971 | 0.5938 |
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| 0.4594 | 122.0 | 122 | 0.5906 | 0.5938 |
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| 175 |
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| 0.4594 | 123.0 | 123 | 0.5841 | 0.5938 |
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| 176 |
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| 0.4594 | 124.0 | 124 | 0.5759 | 0.6562 |
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| 177 |
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| 0.4594 | 125.0 | 125 | 0.5682 | 0.6875 |
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| 178 |
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| 0.4594 | 126.0 | 126 | 0.5566 | 0.6875 |
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| 179 |
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| 0.4594 | 127.0 | 127 | 0.5431 | 0.6875 |
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| 180 |
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| 0.4594 | 128.0 | 128 | 0.5314 | 0.6875 |
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| 181 |
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| 0.4594 | 129.0 | 129 | 0.5221 | 0.7188 |
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| 182 |
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| 0.33 | 130.0 | 130 | 0.5145 | 0.7188 |
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| 183 |
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| 0.33 | 131.0 | 131 | 0.5062 | 0.7188 |
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| 184 |
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| 0.33 | 132.0 | 132 | 0.4988 | 0.7188 |
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| 185 |
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| 0.33 | 133.0 | 133 | 0.4888 | 0.7188 |
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| 186 |
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| 0.33 | 134.0 | 134 | 0.4689 | 0.7188 |
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| 187 |
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| 0.33 | 135.0 | 135 | 0.4586 | 0.75 |
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| 188 |
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| 0.33 | 136.0 | 136 | 0.4464 | 0.7812 |
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| 189 |
+
| 0.33 | 137.0 | 137 | 0.4330 | 0.7812 |
|
| 190 |
+
| 0.33 | 138.0 | 138 | 0.4185 | 0.7812 |
|
| 191 |
+
| 0.33 | 139.0 | 139 | 0.4004 | 0.8125 |
|
| 192 |
+
| 0.2099 | 140.0 | 140 | 0.3852 | 0.8125 |
|
| 193 |
+
| 0.2099 | 141.0 | 141 | 0.3724 | 0.8125 |
|
| 194 |
+
| 0.2099 | 142.0 | 142 | 0.3610 | 0.8125 |
|
| 195 |
+
| 0.2099 | 143.0 | 143 | 0.3613 | 0.8125 |
|
| 196 |
+
| 0.2099 | 144.0 | 144 | 0.3731 | 0.7812 |
|
| 197 |
+
| 0.2099 | 145.0 | 145 | 0.3655 | 0.8125 |
|
| 198 |
+
| 0.2099 | 146.0 | 146 | 0.3553 | 0.8125 |
|
| 199 |
+
| 0.2099 | 147.0 | 147 | 0.3457 | 0.8125 |
|
| 200 |
+
| 0.2099 | 148.0 | 148 | 0.3380 | 0.8438 |
|
| 201 |
+
| 0.2099 | 149.0 | 149 | 0.3315 | 0.8438 |
|
| 202 |
+
| 0.0894 | 150.0 | 150 | 0.3222 | 0.8438 |
|
| 203 |
|
| 204 |
|
| 205 |
### Framework versions
|