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  1. README.md +25 -15
  2. config.json +2 -8
  3. pytorch_model.bin +2 -2
  4. training_args.bin +1 -1
README.md CHANGED
@@ -2,7 +2,7 @@
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  tags:
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  - generated_from_trainer
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  datasets:
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- - null
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  metrics:
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  - precision
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  - recall
@@ -14,10 +14,14 @@ model_index:
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  - task:
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  name: Token Classification
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  type: token-classification
 
 
 
 
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  metric:
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  name: Accuracy
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  type: accuracy
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- value: 0.9418150723128973
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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
@@ -25,13 +29,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # electra-srb-ner
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- This model was trained from scratch on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2062
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- - Precision: 0.7553
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- - Recall: 0.7362
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- - F1: 0.7456
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- - Accuracy: 0.9418
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  ## Model description
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@@ -56,16 +60,22 @@ The following hyperparameters were used during training:
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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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- - num_epochs: 4
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  ### Training results
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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 | 207 | 0.2894 | 0.7278 | 0.6252 | 0.6726 | 0.9207 |
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- | No log | 2.0 | 414 | 0.2175 | 0.7463 | 0.6984 | 0.7216 | 0.9352 |
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- | 0.3035 | 3.0 | 621 | 0.2189 | 0.7826 | 0.7049 | 0.7417 | 0.9398 |
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- | 0.3035 | 4.0 | 828 | 0.2062 | 0.7553 | 0.7362 | 0.7456 | 0.9418 |
 
 
 
 
 
 
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  ### Framework versions
 
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  tags:
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  - generated_from_trainer
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  datasets:
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+ - wikiann
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  metrics:
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  - precision
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  - recall
 
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  - task:
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  name: Token Classification
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  type: token-classification
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+ dataset:
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+ name: wikiann
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+ type: wikiann
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+ args: sr
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  metric:
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  name: Accuracy
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  type: accuracy
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+ value: 0.95641898994996
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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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  # electra-srb-ner
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+ This model was trained from scratch on the wikiann dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3017
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+ - Precision: 0.8911
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+ - Recall: 0.9081
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+ - F1: 0.8995
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+ - Accuracy: 0.9564
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  ## Model description
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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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+ - num_epochs: 10
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.2535 | 1.0 | 1250 | 0.2015 | 0.8494 | 0.8605 | 0.8549 | 0.9376 |
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+ | 0.1461 | 2.0 | 2500 | 0.1853 | 0.8800 | 0.8681 | 0.8740 | 0.9464 |
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+ | 0.0914 | 3.0 | 3750 | 0.2022 | 0.8695 | 0.8912 | 0.8802 | 0.9485 |
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+ | 0.0545 | 4.0 | 5000 | 0.2214 | 0.8758 | 0.8975 | 0.8865 | 0.9514 |
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+ | 0.0385 | 5.0 | 6250 | 0.2536 | 0.8806 | 0.9010 | 0.8907 | 0.9523 |
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+ | 0.0266 | 6.0 | 7500 | 0.2506 | 0.8834 | 0.9020 | 0.8926 | 0.9539 |
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+ | 0.0133 | 7.0 | 8750 | 0.2745 | 0.8910 | 0.9057 | 0.8983 | 0.9562 |
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+ | 0.0077 | 8.0 | 10000 | 0.2946 | 0.8872 | 0.9065 | 0.8968 | 0.9559 |
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+ | 0.0043 | 9.0 | 11250 | 0.2931 | 0.8902 | 0.9094 | 0.8997 | 0.9567 |
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+ | 0.0022 | 10.0 | 12500 | 0.3017 | 0.8911 | 0.9081 | 0.8995 | 0.9564 |
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  ### Framework versions
config.json CHANGED
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  "3": "LABEL_3",
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  "4": "LABEL_4",
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  "5": "LABEL_5",
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- "6": "LABEL_6",
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- "7": "LABEL_7",
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- "8": "LABEL_8",
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- "9": "LABEL_9"
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  },
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  "initializer_range": 0.02,
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  "intermediate_size": 3072,
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  "LABEL_3": 3,
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  "LABEL_4": 4,
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  "LABEL_5": 5,
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- "LABEL_6": 6,
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- "LABEL_7": 7,
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- "LABEL_8": 8,
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- "LABEL_9": 9
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  },
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  "layer_norm_eps": 1e-12,
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  "max_position_embeddings": 512,
 
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  "3": "LABEL_3",
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  "4": "LABEL_4",
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  "5": "LABEL_5",
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+ "6": "LABEL_6"
 
 
 
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  },
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  "initializer_range": 0.02,
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  "intermediate_size": 3072,
 
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  "LABEL_3": 3,
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  "LABEL_4": 4,
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  "LABEL_5": 5,
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+ "LABEL_6": 6
 
 
 
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  },
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  "layer_norm_eps": 1e-12,
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  "max_position_embeddings": 512,
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