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Hebrew_affix_find2

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bert
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  2. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: cc-by-4.0
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+ base_model: dicta-il/dictabert
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - accuracy
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+ model-index:
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+ - name: Hebrew_affix_find2
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+ results: []
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+ ---
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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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+ # Hebrew_affix_find2
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+
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+ This model is a fine-tuned version of [dicta-il/dictabert](https://huggingface.co/dicta-il/dictabert) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0534
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+ - Precision: 0.4646
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+ - Recall: 0.1822
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+ - F1-micro: 0.0791
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+ - F1-macro: 0.1818
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+ - F1 Remove Empty: 0.2674
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+ - Accuracy: 0.9087
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+ - Precision Complex: 0.2664
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+ - Recall Complex: 0.1857
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+ - F1-micro Complex: 0.2060
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+ - F1-macro Complex: 0.1992
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+ - Accuracy Complex: 0.8184
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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+ - train_batch_size: 84
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+ - eval_batch_size: 42
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+ - seed: 42
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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: 50
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+ - num_epochs: 15
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1-micro | F1-macro | F1 Remove Empty | Accuracy | Precision Complex | Recall Complex | F1-micro Complex | F1-macro Complex | Accuracy Complex |
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+ |:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|:---------------:|:--------:|:-----------------:|:--------------:|:----------------:|:----------------:|:----------------:|
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+ | 0.2602 | 0.5995 | 250 | 0.2843 | 0.0729 | 0.0769 | 0.0423 | 0.0711 | 0.2540 | 0.9477 | 0.1411 | 0.1042 | 0.1633 | 0.1149 | 0.8174 |
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+ | 0.1528 | 1.1990 | 500 | 0.1881 | 0.1228 | 0.1311 | 0.0536 | 0.1034 | 0.2873 | 0.9481 | 0.1637 | 0.1376 | 0.1812 | 0.1467 | 0.8177 |
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+ | 0.1302 | 1.7986 | 750 | 0.1366 | 0.1447 | 0.1498 | 0.0601 | 0.1109 | 0.2773 | 0.9478 | 0.1548 | 0.1456 | 0.1871 | 0.1451 | 0.8170 |
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+ | 0.0844 | 2.3981 | 1000 | 0.1108 | 0.1677 | 0.1572 | 0.0638 | 0.1195 | 0.2716 | 0.9478 | 0.1327 | 0.1471 | 0.1915 | 0.1371 | 0.8173 |
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+ | 0.091 | 2.9976 | 1250 | 0.0950 | 0.1728 | 0.1911 | 0.0660 | 0.1273 | 0.2653 | 0.9479 | 0.1291 | 0.1568 | 0.1950 | 0.1398 | 0.8172 |
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+ | 0.0608 | 3.5971 | 1500 | 0.0819 | 0.2912 | 0.1809 | 0.0688 | 0.1668 | 0.2979 | 0.9485 | 0.2092 | 0.1963 | 0.1999 | 0.1995 | 0.8179 |
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+ | 0.047 | 4.1966 | 1750 | 0.0740 | 0.3111 | 0.2112 | 0.0695 | 0.1731 | 0.2885 | 0.9484 | 0.1919 | 0.1965 | 0.1997 | 0.1888 | 0.8178 |
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+ | 0.0421 | 4.7962 | 2000 | 0.0681 | 0.4088 | 0.2131 | 0.0709 | 0.1804 | 0.2652 | 0.9485 | 0.1875 | 0.1968 | 0.2020 | 0.1875 | 0.8181 |
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+ | 0.0345 | 5.3957 | 2250 | 0.0637 | 0.4310 | 0.2131 | 0.0714 | 0.1831 | 0.2692 | 0.9486 | 0.2172 | 0.1963 | 0.2027 | 0.2004 | 0.8183 |
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+ | 0.0353 | 5.9952 | 2500 | 0.0598 | 0.3737 | 0.2145 | 0.0719 | 0.1850 | 0.2721 | 0.9486 | 0.2013 | 0.1965 | 0.2032 | 0.1948 | 0.8183 |
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+ | 0.0291 | 6.5947 | 2750 | 0.0581 | 0.4234 | 0.2141 | 0.0720 | 0.1844 | 0.2712 | 0.9486 | 0.1943 | 0.1967 | 0.2039 | 0.1907 | 0.8184 |
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+ | 0.024 | 7.1942 | 3000 | 0.0572 | 0.4579 | 0.1839 | 0.0721 | 0.1846 | 0.2885 | 0.9487 | 0.2161 | 0.1961 | 0.2050 | 0.2018 | 0.8186 |
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+ | 0.0226 | 7.7938 | 3250 | 0.0555 | 0.4448 | 0.2041 | 0.0725 | 0.1946 | 0.2861 | 0.9487 | 0.2136 | 0.1963 | 0.2078 | 0.2005 | 0.8189 |
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+ | 0.0201 | 8.3933 | 3500 | 0.0558 | 0.4801 | 0.2140 | 0.0725 | 0.1962 | 0.2885 | 0.9487 | 0.2083 | 0.1955 | 0.2057 | 0.1981 | 0.8187 |
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+ | 0.0192 | 8.9928 | 3750 | 0.0546 | 0.4622 | 0.1841 | 0.0727 | 0.1913 | 0.2989 | 0.9488 | 0.2285 | 0.1961 | 0.2072 | 0.2065 | 0.8189 |
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+ | 0.0168 | 9.5923 | 4000 | 0.0551 | 0.4936 | 0.2038 | 0.0728 | 0.2026 | 0.2980 | 0.9487 | 0.2335 | 0.1963 | 0.2083 | 0.2058 | 0.8190 |
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+ | 0.015 | 10.1918 | 4250 | 0.0545 | 0.4590 | 0.1944 | 0.0728 | 0.1970 | 0.2897 | 0.9487 | 0.2315 | 0.1963 | 0.2054 | 0.2040 | 0.8186 |
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+ | 0.0145 | 10.7914 | 4500 | 0.0559 | 0.4623 | 0.1942 | 0.0730 | 0.2002 | 0.2944 | 0.9488 | 0.2284 | 0.1966 | 0.2093 | 0.2069 | 0.8190 |
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+ | 0.015 | 11.3909 | 4750 | 0.0578 | 0.4792 | 0.1843 | 0.0730 | 0.1908 | 0.2982 | 0.9488 | 0.2296 | 0.1966 | 0.2081 | 0.2058 | 0.8188 |
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+ | 0.0146 | 11.9904 | 5000 | 0.0545 | 0.4733 | 0.1942 | 0.0731 | 0.1978 | 0.2909 | 0.9488 | 0.2088 | 0.1966 | 0.2080 | 0.1956 | 0.8188 |
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+ | 0.0138 | 12.5899 | 5250 | 0.0549 | 0.4984 | 0.1940 | 0.0732 | 0.1990 | 0.2927 | 0.9488 | 0.2171 | 0.1966 | 0.2090 | 0.1987 | 0.8189 |
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+ | 0.0116 | 13.1894 | 5500 | 0.0555 | 0.4604 | 0.1844 | 0.0733 | 0.1910 | 0.2984 | 0.9488 | 0.2171 | 0.1966 | 0.2086 | 0.1991 | 0.8189 |
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+ | 0.0122 | 13.7890 | 5750 | 0.0545 | 0.4992 | 0.1943 | 0.0733 | 0.1985 | 0.2919 | 0.9488 | 0.2156 | 0.1966 | 0.2088 | 0.1998 | 0.8189 |
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+ | 0.0113 | 14.3885 | 6000 | 0.0554 | 0.5054 | 0.1943 | 0.0733 | 0.2005 | 0.2948 | 0.9488 | 0.2290 | 0.1966 | 0.2087 | 0.2033 | 0.8189 |
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+ | 0.0107 | 14.9880 | 6250 | 0.0556 | 0.5085 | 0.1943 | 0.0734 | 0.2010 | 0.2956 | 0.9488 | 0.2302 | 0.1966 | 0.2092 | 0.2040 | 0.8190 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.2
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
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