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HueyNemud/icdar23-entrydetector_plaintext_breaks_indents_left_diff
icdar23-entrydetector_plaintext_breaks_indents_left_diff is a token classification model from HueyNemud. Use it when you need labels on individual words, such as names. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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From the Hugging Face model README
This model is a fine-tuned version of HueyNemud/das22-10-camembert_pretrained on the None dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 0.07 | 300 | 0.0309 | 0.9593 | 0.9879 | 0.9734 | 0.9955 |
| 0.161 | 0.14 | 600 | 0.0126 | 0.9890 | 0.9911 | 0.9900 | 0.9982 |
| 0.161 | 0.21 | 900 | 0.0116 | 0.9730 | 0.9894 | 0.9811 | 0.9971 |
| 0.0165 | 0.29 | 1200 | 0.0087 | 0.9938 | 0.9918 | 0.9928 | 0.9987 |
| 0.0119 | 0.36 | 1500 | 0.0093 | 0.9851 | 0.9937 | 0.9894 | 0.9981 |
| 0.0119 | 0.43 | 1800 | 0.0055 | 0.9942 | 0.9913 | 0.9928 | 0.9987 |
| 0.0091 | 0.5 | 2100 | 0.0057 | 0.9951 | 0.9904 | 0.9928 | 0.9987 |
| 0.0091 | 0.57 | 2400 | 0.0058 | 0.9920 | 0.9936 | 0.9928 | 0.9987 |
| 0.0083 | 0.64 | 2700 | 0.0059 | 0.9896 | 0.9918 | 0.9907 | 0.9983 |
| 0.0065 | 0.72 | 3000 | 0.0045 | 0.9968 | 0.9917 | 0.9942 | 0.9990 |
| 0.0065 | 0.79 | 3300 | 0.0047 | 0.9920 | 0.9937 | 0.9929 | 0.9987 |
| 0.0054 | 0.86 | 3600 | 0.0050 | 0.9945 | 0.9909 | 0.9926 | 0.9987 |
| 0.0054 | 0.93 | 3900 | 0.0064 | 0.9838 | 0.9968 | 0.9903 | 0.9983 |
| 0.0056 | 1.0 | 4200 | 0.0046 | 0.9971 | 0.9920 | 0.9946 | 0.9990 |
| 0.0034 | 1.07 | 4500 | 0.0037 | 0.9959 | 0.9936 | 0.9948 | 0.9990 |
| 0.0034 | 1.14 | 4800 | 0.0047 | 0.9983 | 0.9900 | 0.9941 | 0.9989 |
| 0.0035 | 1.22 | 5100 | 0.0043 | 0.9936 | 0.9951 | 0.9944 | 0.9990 |
| 0.0035 | 1.29 | 5400 | 0.0061 | 0.9892 | 0.9957 | 0.9925 | 0.9986 |
| 0.002 | 1.36 | 5700 | 0.0057 | 0.9898 | 0.9947 | 0.9923 | 0.9986 |
| 0.0048 | 1.43 | 6000 | 0.0042 | 0.9954 | 0.9933 | 0.9944 | 0.9990 |