lilyyellow
/

my_awesome_ner-token_classification_v1.0

Token Classification

Generated from Trainer

Inference Endpoints

Model card Files Files and versions Community

lilyyellow commited on 22 days ago

Commit

e86a18c

•

1 Parent(s): bffb9aa

End of training

Files changed (1) hide show

README.md +15 -10

README.md CHANGED Viewed

@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
 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.
 It achieves the following results on the evaluation set:
-- Loss: 0.5013
-- Precision: 0.5533
-- Recall: 0.6351
-- F1: 0.5914
-- Accuracy: 0.8419
 ## Model description
@@ -48,14 +48,19 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: cosine
-- num_epochs: 5
 ### Training results
-| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.4831        | 1.9990 | 2099 | 0.4981          | 0.5509    | 0.6224 | 0.5845 | 0.8411   |
-| 0.391         | 3.9981 | 4198 | 0.5013          | 0.5533    | 0.6351 | 0.5914 | 0.8419   |
 ### Framework versions

 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.
 It achieves the following results on the evaluation set:
+- Loss: 0.7184
+- Precision: 0.5603
+- Recall: 0.6333
+- F1: 0.5946
+- Accuracy: 0.8261
 ## Model description
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: cosine
+- num_epochs: 15
 ### Training results
+| Training Loss | Epoch   | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.478         | 1.9991  | 2313  | 0.5109          | 0.5864    | 0.5956 | 0.5910 | 0.8411   |
+| 0.3927        | 3.9983  | 4626  | 0.5095          | 0.5834    | 0.6186 | 0.6005 | 0.8401   |
+| 0.3111        | 5.9974  | 6939  | 0.5813          | 0.5434    | 0.6335 | 0.5850 | 0.8258   |
+| 0.2339        | 7.9965  | 9252  | 0.6137          | 0.5515    | 0.6312 | 0.5887 | 0.8261   |
+| 0.1983        | 9.9957  | 11565 | 0.6743          | 0.5719    | 0.6243 | 0.5969 | 0.8305   |
+| 0.1565        | 11.9948 | 13878 | 0.7044          | 0.5594    | 0.6372 | 0.5958 | 0.8250   |
+| 0.1657        | 13.9939 | 16191 | 0.7184          | 0.5603    | 0.6333 | 0.5946 | 0.8261   |
 ### Framework versions