End of training

README.md

@@ -1,5 +1,6 @@
 ---
-base_model: NlpHUST/ner-vietnamese-electra-base
+license: mit
+base_model: FacebookAI/xlm-roberta-base
 tags:
 - generated_from_trainer
 metrics:
@@ -17,13 +18,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # my_awesome_ner-token_classification_v1.0
 
-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.
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.0564
-- Precision: 0.3927
-- Recall: 0.3568
-- F1: 0.3739
-- Accuracy: 0.7604
+- Loss: 0.8650
+- Precision: 0.4582
+- Recall: 0.5502
+- F1: 0.5
+- Accuracy: 0.8053
 
 ## Model description
 
@@ -48,17 +49,20 @@ 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
+- num_epochs: 20
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 1.5012        | 1.0   | 113  | 1.4692          | 0.4388    | 0.2417 | 0.3117 | 0.7136   |
-| 1.2038        | 2.0   | 226  | 1.1948          | 0.3838    | 0.3237 | 0.3512 | 0.7469   |
-| 1.1061        | 3.0   | 339  | 1.0912          | 0.4200    | 0.3402 | 0.3759 | 0.7603   |
-| 0.9903        | 4.0   | 452  | 1.0600          | 0.4081    | 0.3548 | 0.3796 | 0.7608   |
-| 0.9847        | 5.0   | 565  | 1.0564          | 0.3927    | 0.3568 | 0.3739 | 0.7604   |
+| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.0426        | 1.9912  | 225  | 0.8857          | 0.3633    | 0.3365 | 0.3494 | 0.7753   |
+| 0.7028        | 3.9823  | 450  | 0.7244          | 0.4994    | 0.4647 | 0.4815 | 0.8136   |
+| 0.5281        | 5.9735  | 675  | 0.6965          | 0.4933    | 0.5513 | 0.5207 | 0.8124   |
+| 0.3767        | 7.9646  | 900  | 0.7331          | 0.4760    | 0.5406 | 0.5063 | 0.8169   |
+| 0.2995        | 9.9558  | 1125 | 0.7731          | 0.4646    | 0.5321 | 0.4960 | 0.8158   |
+| 0.2731        | 11.9469 | 1350 | 0.8100          | 0.4650    | 0.5395 | 0.4995 | 0.8074   |
+| 0.2259        | 13.9381 | 1575 | 0.8500          | 0.4769    | 0.5502 | 0.5109 | 0.8112   |
+| 0.1916        | 15.9292 | 1800 | 0.8650          | 0.4582    | 0.5502 | 0.5    | 0.8053   |
 
 
 ### Framework versions