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BahAdoR0101
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my_xlm-roberta-large-finetuned-conll03

Token Classification
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PyTorch
xlm-roberta
Generated from Trainer
Eval Results
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Files changed (2) hide show
  1. README.md +13 -12
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -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: 0.9136102902833304
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  - name: Recall
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  type: recall
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- value: 0.9305949008498584
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  - name: F1
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  type: f1
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- value: 0.9220243838259802
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  - name: Accuracy
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  type: accuracy
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- value: 0.9833530741897276
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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 [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1119
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- - Precision: 0.9136
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- - Recall: 0.9306
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- - F1: 0.9220
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- - Accuracy: 0.9834
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  ## Model description
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@@ -73,14 +73,15 @@ 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: 2
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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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- | 0.2235 | 1.0 | 878 | 0.1090 | 0.8998 | 0.9125 | 0.9061 | 0.9812 |
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- | 0.0302 | 2.0 | 1756 | 0.1119 | 0.9136 | 0.9306 | 0.9220 | 0.9834 |
 
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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: 0.9244064245810056
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  - name: Recall
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  type: recall
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+ value: 0.9375
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  - name: F1
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  type: f1
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+ value: 0.9309071729957805
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9856142995585226
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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 conll2003 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1185
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+ - Precision: 0.9244
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+ - Recall: 0.9375
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+ - F1: 0.9309
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+ - Accuracy: 0.9856
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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: 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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+ | 0.1533 | 1.0 | 878 | 0.1178 | 0.8950 | 0.9053 | 0.9001 | 0.9805 |
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+ | 0.0303 | 2.0 | 1756 | 0.1157 | 0.9157 | 0.9331 | 0.9243 | 0.9843 |
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+ | 0.0164 | 3.0 | 2634 | 0.1185 | 0.9244 | 0.9375 | 0.9309 | 0.9856 |
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  ### Framework versions
pytorch_model.bin CHANGED
@@ -1,3 +1,3 @@
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