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almaghrabima
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ner_column_bert-base-NER

Token Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Inference Endpoints
Model card Files Community
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  1. README.md +30 -41
  2. pytorch_model.bin +1 -1
README.md CHANGED
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  ---
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  license: mit
 
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  tags:
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  - generated_from_trainer
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  metrics:
@@ -10,18 +11,6 @@ metrics:
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  model-index:
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  - name: ner_column_bert-base-NER
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  results: []
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- language:
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- - en
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- widget:
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- - >-
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- bestseller 620463000001
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- - >-
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- - >-
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- fashion leather co ltd
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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
@@ -31,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1872
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- - Precision: 0.7623
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- - Recall: 0.7753
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- - F1: 0.7688
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- - Accuracy: 0.9023
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  ## Model description
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@@ -66,31 +55,31 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 702 | 0.6427 | 0.3025 | 0.2180 | 0.2534 | 0.7415 |
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- | 0.9329 | 2.0 | 1404 | 0.4771 | 0.4343 | 0.3587 | 0.3929 | 0.7955 |
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- | 0.546 | 3.0 | 2106 | 0.3983 | 0.5157 | 0.4530 | 0.4823 | 0.8242 |
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- | 0.546 | 4.0 | 2808 | 0.3748 | 0.5089 | 0.4758 | 0.4918 | 0.8305 |
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- | 0.4339 | 5.0 | 3510 | 0.2947 | 0.6362 | 0.6146 | 0.6252 | 0.8656 |
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- | 0.3658 | 6.0 | 4212 | 0.2818 | 0.6421 | 0.6231 | 0.6325 | 0.8664 |
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- | 0.3658 | 7.0 | 4914 | 0.2459 | 0.7108 | 0.6983 | 0.7045 | 0.8834 |
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- | 0.3221 | 8.0 | 5616 | 0.2665 | 0.6586 | 0.6404 | 0.6494 | 0.8701 |
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- | 0.2914 | 9.0 | 6318 | 0.2449 | 0.6880 | 0.6768 | 0.6823 | 0.8793 |
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- | 0.2657 | 10.0 | 7020 | 0.2411 | 0.7014 | 0.6862 | 0.6937 | 0.8824 |
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- | 0.2657 | 11.0 | 7722 | 0.2179 | 0.7261 | 0.7228 | 0.7244 | 0.8902 |
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- | 0.2453 | 12.0 | 8424 | 0.2301 | 0.6922 | 0.6919 | 0.6920 | 0.8858 |
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- | 0.2295 | 13.0 | 9126 | 0.2352 | 0.6768 | 0.6836 | 0.6802 | 0.8832 |
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- | 0.2295 | 14.0 | 9828 | 0.2020 | 0.7545 | 0.7499 | 0.7522 | 0.8970 |
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- | 0.2155 | 15.0 | 10530 | 0.2012 | 0.7449 | 0.7508 | 0.7478 | 0.8974 |
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- | 0.2064 | 16.0 | 11232 | 0.2036 | 0.7282 | 0.7402 | 0.7341 | 0.8960 |
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- | 0.2064 | 17.0 | 11934 | 0.1976 | 0.7390 | 0.7496 | 0.7443 | 0.8974 |
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- | 0.1978 | 18.0 | 12636 | 0.1859 | 0.7688 | 0.7828 | 0.7757 | 0.9040 |
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- | 0.1895 | 19.0 | 13338 | 0.1917 | 0.7574 | 0.7691 | 0.7632 | 0.9014 |
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- | 0.186 | 20.0 | 14040 | 0.1872 | 0.7623 | 0.7753 | 0.7688 | 0.9023 |
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  ### Framework versions
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- - Transformers 4.30.2
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- - Pytorch 1.13.1+cu116
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- - Datasets 2.13.2
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- - Tokenizers 0.13.3
 
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  ---
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  license: mit
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+ base_model: dslim/bert-base-NER
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  tags:
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  - generated_from_trainer
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  metrics:
 
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  model-index:
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  - name: ner_column_bert-base-NER
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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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  This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1855
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+ - Precision: 0.7651
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+ - Recall: 0.7786
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+ - F1: 0.7718
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+ - Accuracy: 0.9026
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 702 | 0.7382 | 0.2576 | 0.1887 | 0.2178 | 0.7127 |
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+ | 0.9356 | 2.0 | 1404 | 0.4405 | 0.5139 | 0.4331 | 0.4700 | 0.8157 |
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+ | 0.5445 | 3.0 | 2106 | 0.3608 | 0.5712 | 0.5143 | 0.5413 | 0.8404 |
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+ | 0.5445 | 4.0 | 2808 | 0.3226 | 0.6188 | 0.5840 | 0.6009 | 0.8550 |
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+ | 0.4316 | 5.0 | 3510 | 0.2757 | 0.6788 | 0.6569 | 0.6676 | 0.8728 |
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+ | 0.3605 | 6.0 | 4212 | 0.2828 | 0.6584 | 0.6346 | 0.6463 | 0.8697 |
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+ | 0.3605 | 7.0 | 4914 | 0.2456 | 0.7108 | 0.6926 | 0.7015 | 0.8820 |
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+ | 0.3153 | 8.0 | 5616 | 0.2385 | 0.7055 | 0.6986 | 0.7021 | 0.8855 |
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+ | 0.282 | 9.0 | 6318 | 0.2345 | 0.7044 | 0.6961 | 0.7002 | 0.8853 |
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+ | 0.2587 | 10.0 | 7020 | 0.2313 | 0.7081 | 0.7049 | 0.7065 | 0.8862 |
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+ | 0.2587 | 11.0 | 7722 | 0.2026 | 0.7734 | 0.7537 | 0.7634 | 0.8968 |
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+ | 0.239 | 12.0 | 8424 | 0.1980 | 0.7651 | 0.7687 | 0.7669 | 0.8991 |
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+ | 0.2241 | 13.0 | 9126 | 0.2091 | 0.7368 | 0.7423 | 0.7395 | 0.8936 |
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+ | 0.2241 | 14.0 | 9828 | 0.1954 | 0.7693 | 0.7684 | 0.7689 | 0.8987 |
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+ | 0.2124 | 15.0 | 10530 | 0.1916 | 0.7668 | 0.7749 | 0.7708 | 0.9008 |
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+ | 0.2025 | 16.0 | 11232 | 0.1841 | 0.7699 | 0.7794 | 0.7746 | 0.9024 |
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+ | 0.2025 | 17.0 | 11934 | 0.1938 | 0.7527 | 0.7626 | 0.7576 | 0.8992 |
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+ | 0.193 | 18.0 | 12636 | 0.1849 | 0.7705 | 0.7841 | 0.7772 | 0.9040 |
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+ | 0.1877 | 19.0 | 13338 | 0.1927 | 0.7510 | 0.7649 | 0.7579 | 0.9005 |
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+ | 0.1821 | 20.0 | 14040 | 0.1855 | 0.7651 | 0.7786 | 0.7718 | 0.9026 |
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  ### Framework versions
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+ - Transformers 4.33.2
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.14.5
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+ - Tokenizers 0.13.3
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