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pritmanvar
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ner_roberta_model

Token Classification
Transformers
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Safetensors
xlm-roberta
Generated from Trainer
Eval Results
Inference Endpoints
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End of training

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  1. README.md +90 -12
  2. config.json +20 -24
  3. model.safetensors +2 -2
  4. runs/Mar27_05-21-34_757ff407108b/events.out.tfevents.1711516907.757ff407108b.1998.0 +3 -0
  5. runs/Mar27_05-22-19_757ff407108b/events.out.tfevents.1711516944.757ff407108b.1998.1 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -5,9 +5,36 @@ tags:
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  - generated_from_trainer
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  datasets:
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  - shipping_label_ner
 
 
 
 
 
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  model-index:
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  - name: ner_roberta_model
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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
@@ -17,16 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the shipping_label_ner dataset.
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  It achieves the following results on the evaluation set:
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- - eval_loss: 0.0031
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- - eval_precision: 1.0
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- - eval_recall: 1.0
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- - eval_f1: 1.0
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- - eval_accuracy: 1.0
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- - eval_runtime: 0.3242
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- - eval_samples_per_second: 15.424
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- - eval_steps_per_second: 9.255
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- - epoch: 23.0
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- - step: 230
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  ## Model description
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@@ -51,7 +73,63 @@ 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: 100
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - generated_from_trainer
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  datasets:
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  - shipping_label_ner
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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  model-index:
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  - name: ner_roberta_model
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+ results:
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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: shipping_label_ner
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+ type: shipping_label_ner
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+ config: shipping_label_ner
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+ split: validation
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+ args: shipping_label_ner
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.5272727272727272
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+ - name: Recall
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+ type: recall
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+ value: 0.7837837837837838
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+ - name: F1
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+ type: f1
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+ value: 0.6304347826086956
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.7796610169491526
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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 [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the shipping_label_ner dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.0623
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+ - Precision: 0.5273
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+ - Recall: 0.7838
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+ - F1: 0.6304
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+ - Accuracy: 0.7797
 
 
 
 
 
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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: 50
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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 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 14 | 1.1206 | 0.3125 | 0.4054 | 0.3529 | 0.6610 |
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+ | No log | 2.0 | 28 | 0.7363 | 0.5128 | 0.5405 | 0.5263 | 0.7119 |
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+ | No log | 3.0 | 42 | 0.6219 | 0.5333 | 0.6486 | 0.5854 | 0.7542 |
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+ | No log | 4.0 | 56 | 0.7328 | 0.4727 | 0.7027 | 0.5652 | 0.7627 |
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+ | No log | 5.0 | 70 | 0.8181 | 0.5 | 0.7297 | 0.5934 | 0.7542 |
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+ | No log | 6.0 | 84 | 0.8485 | 0.5185 | 0.7568 | 0.6154 | 0.7627 |
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+ | No log | 7.0 | 98 | 0.9692 | 0.5 | 0.7027 | 0.5843 | 0.7542 |
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+ | No log | 8.0 | 112 | 0.9842 | 0.4915 | 0.7838 | 0.6042 | 0.7458 |
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+ | No log | 9.0 | 126 | 1.1196 | 0.5 | 0.7838 | 0.6105 | 0.7542 |
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+ | No log | 10.0 | 140 | 1.2147 | 0.5 | 0.7838 | 0.6105 | 0.7542 |
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+ | No log | 11.0 | 154 | 1.4110 | 0.5 | 0.7568 | 0.6022 | 0.7712 |
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+ | No log | 12.0 | 168 | 1.2104 | 0.5370 | 0.7838 | 0.6374 | 0.7881 |
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+ | No log | 13.0 | 182 | 1.4145 | 0.5283 | 0.7568 | 0.6222 | 0.7797 |
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+ | No log | 14.0 | 196 | 1.4939 | 0.5179 | 0.7838 | 0.6237 | 0.7712 |
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+ | No log | 15.0 | 210 | 1.5558 | 0.5273 | 0.7838 | 0.6304 | 0.7797 |
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+ | No log | 16.0 | 224 | 1.5639 | 0.5273 | 0.7838 | 0.6304 | 0.7797 |
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+ | No log | 17.0 | 238 | 1.5208 | 0.5179 | 0.7838 | 0.6237 | 0.7712 |
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+ | No log | 18.0 | 252 | 1.4787 | 0.5918 | 0.7838 | 0.6744 | 0.7966 |
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+ | No log | 19.0 | 266 | 1.3946 | 0.5283 | 0.7568 | 0.6222 | 0.7797 |
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+ | No log | 20.0 | 280 | 1.6672 | 0.5370 | 0.7838 | 0.6374 | 0.7881 |
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+ | No log | 21.0 | 294 | 1.5746 | 0.5185 | 0.7568 | 0.6154 | 0.7712 |
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+ | No log | 22.0 | 308 | 1.8881 | 0.5091 | 0.7568 | 0.6087 | 0.7712 |
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+ | No log | 23.0 | 322 | 1.5084 | 0.5370 | 0.7838 | 0.6374 | 0.7881 |
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+ | No log | 24.0 | 336 | 1.7922 | 0.5091 | 0.7568 | 0.6087 | 0.7712 |
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+ | No log | 25.0 | 350 | 1.7265 | 0.5273 | 0.7838 | 0.6304 | 0.7797 |
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+ | No log | 26.0 | 364 | 1.7467 | 0.5273 | 0.7838 | 0.6304 | 0.7797 |
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+ | No log | 27.0 | 378 | 2.0162 | 0.5 | 0.7568 | 0.6022 | 0.7627 |
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+ | No log | 28.0 | 392 | 1.9460 | 0.5 | 0.7568 | 0.6022 | 0.7627 |
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+ | No log | 29.0 | 406 | 1.8957 | 0.5091 | 0.7568 | 0.6087 | 0.7712 |
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+ | No log | 30.0 | 420 | 1.9941 | 0.5 | 0.7568 | 0.6022 | 0.7627 |
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+ | No log | 31.0 | 434 | 1.9095 | 0.5 | 0.7568 | 0.6022 | 0.7712 |
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+ | No log | 32.0 | 448 | 1.8920 | 0.5273 | 0.7838 | 0.6304 | 0.7797 |
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+ | No log | 33.0 | 462 | 1.9310 | 0.5091 | 0.7568 | 0.6087 | 0.7712 |
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+ | No log | 34.0 | 476 | 1.9830 | 0.5091 | 0.7568 | 0.6087 | 0.7712 |
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+ | No log | 35.0 | 490 | 2.0445 | 0.5091 | 0.7568 | 0.6087 | 0.7712 |
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+ | 0.2599 | 36.0 | 504 | 2.1138 | 0.5091 | 0.7568 | 0.6087 | 0.7712 |
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+ | 0.2599 | 37.0 | 518 | 2.0024 | 0.5091 | 0.7568 | 0.6087 | 0.7797 |
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+ | 0.2599 | 38.0 | 532 | 2.0004 | 0.5091 | 0.7568 | 0.6087 | 0.7712 |
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+ | 0.2599 | 39.0 | 546 | 2.0725 | 0.5091 | 0.7568 | 0.6087 | 0.7712 |
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+ | 0.2599 | 40.0 | 560 | 2.0507 | 0.5091 | 0.7568 | 0.6087 | 0.7712 |
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+ | 0.2599 | 41.0 | 574 | 2.0548 | 0.5091 | 0.7568 | 0.6087 | 0.7712 |
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+ | 0.2599 | 42.0 | 588 | 2.1176 | 0.5091 | 0.7568 | 0.6087 | 0.7712 |
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+ | 0.2599 | 43.0 | 602 | 2.0946 | 0.5091 | 0.7568 | 0.6087 | 0.7712 |
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+ | 0.2599 | 44.0 | 616 | 2.1211 | 0.5 | 0.7568 | 0.6022 | 0.7627 |
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+ | 0.2599 | 45.0 | 630 | 2.1103 | 0.5091 | 0.7568 | 0.6087 | 0.7712 |
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+ | 0.2599 | 46.0 | 644 | 2.0876 | 0.5 | 0.7568 | 0.6022 | 0.7627 |
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+ | 0.2599 | 47.0 | 658 | 2.0910 | 0.5179 | 0.7838 | 0.6237 | 0.7712 |
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+ | 0.2599 | 48.0 | 672 | 2.0800 | 0.5179 | 0.7838 | 0.6237 | 0.7712 |
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+ | 0.2599 | 49.0 | 686 | 2.0584 | 0.5273 | 0.7838 | 0.6304 | 0.7797 |
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+ | 0.2599 | 50.0 | 700 | 2.0623 | 0.5273 | 0.7838 | 0.6304 | 0.7797 |
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+
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  ### Framework versions
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config.json CHANGED
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  "hidden_size": 1024,
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  "id2label": {
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  "0": "O",
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- "1": "B-GCNUM",
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- "I-NAME": 12,
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  "O": 0
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  },
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  "hidden_size": 1024,
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  "id2label": {
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  "0": "O",
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+ "1": "B-ORG",
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+ "2": "I-ORG",
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+ "7": "B-NAME",
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  "9": "B-LOCATION",
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  "10": "I-LOCATION",
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+ "12": "I-COUNTRY"
 
 
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  "initializer_range": 0.02,
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  "intermediate_size": 4096,
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  "label2id": {
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+ "I-COUNTRY": 12,
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+ "I-GCNUMBER": 4,
 
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+ "I-NAME": 8,
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+ "I-ORG": 2,
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  "O": 0
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  "layer_norm_eps": 1e-05,
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