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UNER_subword_tk_en_lora_alpha_64_drop_0.3_rank_32_seed_42

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@@ -4,7 +4,7 @@ base_model: xlm-roberta-base
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  tags:
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  - generated_from_trainer
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  datasets:
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- - universalner/universal_ner
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  metrics:
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  - precision
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  - recall
@@ -17,24 +17,24 @@ model-index:
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  name: Token Classification
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  type: token-classification
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  dataset:
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- name: universalner/universal_ner en_ewt
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- type: universalner/universal_ner
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  config: en_ewt
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  split: validation
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  args: en_ewt
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.7770204479065238
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  - name: Recall
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  type: recall
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- value: 0.8260869565217391
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  - name: F1
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  type: f1
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- value: 0.8008028098344204
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  - name: Accuracy
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  type: accuracy
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- value: 0.9841743210465624
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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
@@ -42,13 +42,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # UNER_subword_tk_en_lora_alpha_64_drop_0.3_rank_32_seed_42
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- This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the universalner/universal_ner en_ewt dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0645
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- - Precision: 0.7770
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- - Recall: 0.8261
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- - F1: 0.8008
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- - Accuracy: 0.9842
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  ## Model description
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@@ -69,7 +69,7 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0001
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  - train_batch_size: 32
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- - eval_batch_size: 8
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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
@@ -79,26 +79,26 @@ 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 | 392 | 0.0959 | 0.575 | 0.7143 | 0.6371 | 0.9741 |
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- | 0.1782 | 2.0 | 784 | 0.0704 | 0.6961 | 0.7919 | 0.7409 | 0.9798 |
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- | 0.0539 | 3.0 | 1176 | 0.0710 | 0.7144 | 0.8209 | 0.7640 | 0.9814 |
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- | 0.0435 | 4.0 | 1568 | 0.0623 | 0.7170 | 0.8209 | 0.7654 | 0.9821 |
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- | 0.0435 | 5.0 | 1960 | 0.0627 | 0.7321 | 0.8261 | 0.7763 | 0.9820 |
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- | 0.0385 | 6.0 | 2352 | 0.0614 | 0.7398 | 0.8240 | 0.7796 | 0.9826 |
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- | 0.0342 | 7.0 | 2744 | 0.0621 | 0.7433 | 0.8333 | 0.7857 | 0.9829 |
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- | 0.0313 | 8.0 | 3136 | 0.0619 | 0.7752 | 0.8282 | 0.8008 | 0.9847 |
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- | 0.0295 | 9.0 | 3528 | 0.0605 | 0.7578 | 0.8261 | 0.7905 | 0.9834 |
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- | 0.0295 | 10.0 | 3920 | 0.0597 | 0.7776 | 0.8323 | 0.804 | 0.9849 |
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- | 0.0272 | 11.0 | 4312 | 0.0598 | 0.7589 | 0.8344 | 0.7949 | 0.9835 |
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- | 0.0256 | 12.0 | 4704 | 0.0623 | 0.7785 | 0.8188 | 0.7982 | 0.9842 |
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- | 0.0248 | 13.0 | 5096 | 0.0611 | 0.7796 | 0.8240 | 0.8012 | 0.9845 |
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- | 0.0248 | 14.0 | 5488 | 0.0611 | 0.7587 | 0.8333 | 0.7943 | 0.9835 |
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- | 0.023 | 15.0 | 5880 | 0.0624 | 0.7790 | 0.8282 | 0.8028 | 0.9844 |
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- | 0.0222 | 16.0 | 6272 | 0.0637 | 0.7720 | 0.8344 | 0.8020 | 0.9841 |
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- | 0.0205 | 17.0 | 6664 | 0.0642 | 0.7772 | 0.8271 | 0.8014 | 0.9840 |
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- | 0.0207 | 18.0 | 7056 | 0.0644 | 0.7744 | 0.8282 | 0.8004 | 0.9840 |
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- | 0.0207 | 19.0 | 7448 | 0.0651 | 0.7768 | 0.8323 | 0.8036 | 0.9843 |
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- | 0.0203 | 20.0 | 7840 | 0.0645 | 0.7770 | 0.8261 | 0.8008 | 0.9842 |
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  ### Framework versions
 
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  tags:
5
  - generated_from_trainer
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  datasets:
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+ - universal_ner
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  metrics:
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  - precision
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  - recall
 
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  name: Token Classification
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  type: token-classification
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  dataset:
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+ name: universal_ner
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+ type: universal_ner
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  config: en_ewt
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  split: validation
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  args: en_ewt
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.7735665694849369
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  - name: Recall
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  type: recall
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+ value: 0.8240165631469979
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  - name: F1
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  type: f1
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+ value: 0.7979949874686717
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9840550320092251
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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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  # UNER_subword_tk_en_lora_alpha_64_drop_0.3_rank_32_seed_42
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+ This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the universal_ner dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0607
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+ - Precision: 0.7736
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+ - Recall: 0.8240
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+ - F1: 0.7980
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+ - Accuracy: 0.9841
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0001
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  - train_batch_size: 32
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+ - eval_batch_size: 32
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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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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 392 | 0.0899 | 0.5755 | 0.7143 | 0.6374 | 0.9740 |
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+ | 0.1782 | 2.0 | 784 | 0.0651 | 0.6961 | 0.7919 | 0.7409 | 0.9799 |
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+ | 0.0539 | 3.0 | 1176 | 0.0664 | 0.7144 | 0.8209 | 0.7640 | 0.9815 |
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+ | 0.0435 | 4.0 | 1568 | 0.0581 | 0.7170 | 0.8209 | 0.7654 | 0.9821 |
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+ | 0.0435 | 5.0 | 1960 | 0.0584 | 0.7321 | 0.8261 | 0.7763 | 0.9820 |
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+ | 0.0385 | 6.0 | 2352 | 0.0571 | 0.7409 | 0.8230 | 0.7798 | 0.9827 |
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+ | 0.0342 | 7.0 | 2744 | 0.0580 | 0.7433 | 0.8333 | 0.7857 | 0.9829 |
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+ | 0.0313 | 8.0 | 3136 | 0.0578 | 0.7744 | 0.8282 | 0.8004 | 0.9846 |
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+ | 0.0295 | 9.0 | 3528 | 0.0566 | 0.7588 | 0.8271 | 0.7915 | 0.9835 |
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+ | 0.0295 | 10.0 | 3920 | 0.0564 | 0.7756 | 0.8302 | 0.8020 | 0.9848 |
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+ | 0.0272 | 11.0 | 4312 | 0.0557 | 0.7597 | 0.8344 | 0.7953 | 0.9835 |
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+ | 0.0256 | 12.0 | 4704 | 0.0585 | 0.7787 | 0.8157 | 0.7968 | 0.9841 |
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+ | 0.0248 | 13.0 | 5096 | 0.0574 | 0.7812 | 0.8240 | 0.8020 | 0.9845 |
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+ | 0.0248 | 14.0 | 5488 | 0.0577 | 0.7604 | 0.8344 | 0.7957 | 0.9836 |
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+ | 0.023 | 15.0 | 5880 | 0.0583 | 0.7812 | 0.8282 | 0.8040 | 0.9845 |
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+ | 0.0222 | 16.0 | 6272 | 0.0595 | 0.7733 | 0.8333 | 0.8022 | 0.9841 |
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+ | 0.0205 | 17.0 | 6664 | 0.0603 | 0.7755 | 0.8261 | 0.8 | 0.9839 |
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+ | 0.0207 | 18.0 | 7056 | 0.0605 | 0.7744 | 0.8282 | 0.8004 | 0.9840 |
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+ | 0.0207 | 19.0 | 7448 | 0.0611 | 0.7770 | 0.8333 | 0.8042 | 0.9842 |
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+ | 0.0203 | 20.0 | 7840 | 0.0607 | 0.7736 | 0.8240 | 0.7980 | 0.9841 |
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  ### Framework versions