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lilyyellow
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my_awesome_ner-token_classification_v1.0

Token Classification
Transformers
Safetensors
electra
Generated from Trainer
Inference Endpoints
Model card Files Community
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End of training

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@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [NlpHUST/ner-vietnamese-electra-base](https://huggingface.co/NlpHUST/ner-vietnamese-electra-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5013
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- - Precision: 0.5533
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- - Recall: 0.6351
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- - F1: 0.5914
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- - Accuracy: 0.8419
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  ## Model description
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@@ -48,14 +48,19 @@ 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: cosine
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- - num_epochs: 5
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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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- | 0.4831 | 1.9990 | 2099 | 0.4981 | 0.5509 | 0.6224 | 0.5845 | 0.8411 |
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- | 0.391 | 3.9981 | 4198 | 0.5013 | 0.5533 | 0.6351 | 0.5914 | 0.8419 |
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [NlpHUST/ner-vietnamese-electra-base](https://huggingface.co/NlpHUST/ner-vietnamese-electra-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7184
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+ - Precision: 0.5603
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+ - Recall: 0.6333
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+ - F1: 0.5946
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+ - Accuracy: 0.8261
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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: cosine
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+ - num_epochs: 15
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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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+ | 0.478 | 1.9991 | 2313 | 0.5109 | 0.5864 | 0.5956 | 0.5910 | 0.8411 |
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+ | 0.3927 | 3.9983 | 4626 | 0.5095 | 0.5834 | 0.6186 | 0.6005 | 0.8401 |
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+ | 0.3111 | 5.9974 | 6939 | 0.5813 | 0.5434 | 0.6335 | 0.5850 | 0.8258 |
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+ | 0.2339 | 7.9965 | 9252 | 0.6137 | 0.5515 | 0.6312 | 0.5887 | 0.8261 |
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+ | 0.1983 | 9.9957 | 11565 | 0.6743 | 0.5719 | 0.6243 | 0.5969 | 0.8305 |
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+ | 0.1565 | 11.9948 | 13878 | 0.7044 | 0.5594 | 0.6372 | 0.5958 | 0.8250 |
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+ | 0.1657 | 13.9939 | 16191 | 0.7184 | 0.5603 | 0.6333 | 0.5946 | 0.8261 |
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  ### Framework versions