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raulgdp
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roberta-base-bne-capitel-ner

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  1. README.md +14 -13
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@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8601446000903751
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  - name: Recall
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  type: recall
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- value: 0.8747702205882353
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  - name: F1
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  type: f1
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- value: 0.8673957621326043
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  - name: Accuracy
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  type: accuracy
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- value: 0.9779993282237626
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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
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [BSC-LT/roberta-base-bne-capitel-ner](https://huggingface.co/BSC-LT/roberta-base-bne-capitel-ner) on the conll2002 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1229
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- - Precision: 0.8601
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- - Recall: 0.8748
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- - F1: 0.8674
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- - Accuracy: 0.9780
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  ## Model description
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@@ -73,15 +73,16 @@ 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: 3
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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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- | 0.0172 | 1.0 | 1041 | 0.1157 | 0.8468 | 0.8640 | 0.8553 | 0.9770 |
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- | 0.0109 | 2.0 | 2082 | 0.1177 | 0.8705 | 0.8853 | 0.8779 | 0.9786 |
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- | 0.0066 | 3.0 | 3123 | 0.1229 | 0.8601 | 0.8748 | 0.8674 | 0.9780 |
 
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.8712310133756518
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  - name: Recall
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  type: recall
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+ value: 0.8830422794117647
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  - name: F1
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  type: f1
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+ value: 0.8770968846285518
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  - name: Accuracy
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  type: accuracy
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+ value: 0.978961189654646
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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 [BSC-LT/roberta-base-bne-capitel-ner](https://huggingface.co/BSC-LT/roberta-base-bne-capitel-ner) on the conll2002 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1255
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+ - Precision: 0.8712
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+ - Recall: 0.8830
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+ - F1: 0.8771
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+ - Accuracy: 0.9790
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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: 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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+ | 0.0135 | 1.0 | 1041 | 0.1233 | 0.8615 | 0.8803 | 0.8708 | 0.9783 |
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+ | 0.0111 | 2.0 | 2082 | 0.1099 | 0.8709 | 0.8853 | 0.8781 | 0.9799 |
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+ | 0.0061 | 3.0 | 3123 | 0.1203 | 0.8569 | 0.8739 | 0.8653 | 0.9781 |
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+ | 0.0035 | 4.0 | 4164 | 0.1255 | 0.8712 | 0.8830 | 0.8771 | 0.9790 |
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  ### Framework versions