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ner_bert_model

Token Classification
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distilbert
Generated from Trainer
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End of training

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  1. README.md +63 -15
  2. model.safetensors +1 -1
  3. runs/Mar27_05-22-59_4912d2a8deb3/events.out.tfevents.1711517435.4912d2a8deb3.761.1 +2 -2
README.md CHANGED
@@ -1,13 +1,40 @@
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  ---
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- license: mit
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- base_model: xlm-roberta-large
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  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_bert_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
@@ -15,18 +42,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # ner_bert_model
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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: 0.1076
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- - eval_precision: 0.9091
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- - eval_recall: 0.9524
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- - eval_f1: 0.9302
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- - eval_accuracy: 0.9691
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- - eval_runtime: 0.325
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- - eval_samples_per_second: 15.384
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- - eval_steps_per_second: 9.23
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- - epoch: 13.0
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- - step: 130
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  ## Model description
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@@ -51,7 +73,33 @@ 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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  ---
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+ license: apache-2.0
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+ base_model: distilbert-base-cased
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  tags:
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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_bert_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: 0.5178571428571429
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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: 0.6236559139784947
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.7627118644067796
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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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  # ner_bert_model
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+ This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the shipping_label_ner dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2010
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+ - Precision: 0.5179
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+ - Recall: 0.7838
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+ - F1: 0.6237
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+ - Accuracy: 0.7627
 
 
 
 
 
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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: 20
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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 | 0.6828 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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+ | No log | 2.0 | 28 | 0.8587 | 0.5273 | 0.7838 | 0.6304 | 0.7712 |
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+ | No log | 3.0 | 42 | 0.7206 | 0.5577 | 0.7838 | 0.6517 | 0.8136 |
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+ | No log | 4.0 | 56 | 0.8983 | 0.5370 | 0.7838 | 0.6374 | 0.7797 |
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+ | No log | 5.0 | 70 | 0.6964 | 0.5472 | 0.7838 | 0.6444 | 0.8051 |
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+ | No log | 6.0 | 84 | 0.9793 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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+ | No log | 7.0 | 98 | 0.6047 | 0.5472 | 0.7838 | 0.6444 | 0.8051 |
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+ | No log | 8.0 | 112 | 1.0809 | 0.5179 | 0.7838 | 0.6237 | 0.7797 |
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+ | No log | 9.0 | 126 | 1.1726 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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+ | No log | 10.0 | 140 | 1.0067 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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+ | No log | 11.0 | 154 | 1.1439 | 0.5088 | 0.7838 | 0.6170 | 0.7627 |
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+ | No log | 12.0 | 168 | 0.8971 | 0.5370 | 0.7838 | 0.6374 | 0.7881 |
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+ | No log | 13.0 | 182 | 1.0603 | 0.5179 | 0.7838 | 0.6237 | 0.7542 |
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+ | No log | 14.0 | 196 | 1.2095 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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+ | No log | 15.0 | 210 | 1.2395 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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+ | No log | 16.0 | 224 | 1.2509 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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+ | No log | 17.0 | 238 | 1.2317 | 0.5179 | 0.7838 | 0.6237 | 0.7542 |
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+ | No log | 18.0 | 252 | 1.2656 | 0.5179 | 0.7838 | 0.6237 | 0.7542 |
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+ | No log | 19.0 | 266 | 1.1950 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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+ | No log | 20.0 | 280 | 1.2010 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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+
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  ### Framework versions
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