Update README.md

README.md

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-
-\f0\fs24 \cf0 ---\
-tags:\
-- generated_from_trainer\
-metrics:\
-- precision\
-- recall\
-- f1\
-- accuracy\
-model-index:\
-- name: bert-base-greek-uncased-v1-finetuned-ner\
- results: []\
----\
-\
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You\
-should probably proofread and complete it, then remove this comment. -->\
-\
-#bert-base-greek-uncased-v1-finetuned-ner\
-\
-This model is a fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1]({\field{\*\fldinst{HYPERLINK "https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1"}}{\fldrslt https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1}}) on an unknown dataset.\
-It achieves the following results on the evaluation set:\
-- Loss: 0.1052\
-- Precision: 0.8440\
-- Recall: 0.8566\
-- F1: 0.8503\
-- Accuracy: 0.9768\
-\
-## Model description\
-\
-More information needed\
-\
-## Intended uses & limitations\
-\
-More information needed\
-\
-## Training and evaluation data\
-\
-More information needed\
-\
-## Training procedure\
-\
-### Training hyperparameters\
-\
-The following hyperparameters were used during training:\
-- learning_rate: 2e-05\
-- train_batch_size: 16\
-- eval_batch_size: 16\
-- seed: 42\
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\
-- lr_scheduler_type: linear\
-- num_epochs: 10\
-\
-### Training results\
-\
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |\
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|\
-| No log | 0.68 | 250 | 0.0913 | 0.7814 | 0.8596 | 0.8187 | 0.9717 |\
-| 0.1136 | 1.29 | 500 | 0.0823 | 0.7940 | 0.8738 | 0.8320 | 0.9731 |\
-| 0.1136 | 1.93 | 750 | 0.0810 | 0.8057 | 0.8645 | 0.8341 | 0.9737 |\
-| 0.0521 | 2.58 | 1000 | 0.0855 | 0.8244 | 0.8610 | 0.8423 | 0.9752 |\
-| 0.0521 | 3.22 | 1250 | 0.0926 | 0.8329 | 0.8627 | 0.8476 | 0.9762 |\
-| 0.0352 | 3.87 | 1500 | 0.0869 | 0.8286 | 0.8614 | 0.8447 | 0.9755 |\
-| 0.0352 | 4.51 | 1750 | 0.0950 | 0.8446 | 0.8528 | 0.8487 | 0.9751 |\
-| 0.023 | 5.15 | 2000 | 0.1052 | 0.8381 | 0.8586 | 0.8483 | 0.9759 |\
-| 0.023 | 5.8 | 2250 | 0.1049 | 0.8291 | 0.8614 | 0.8449 | 0.9758 |\
-| 0.0158 | 6.44 | 2500 | 0.1158 | 0.8189 | 0.8727 | 0.8450 | 0.9758 |\
-| 0.0158 | 7.09 | 2750 | 0.1248 | 0.8270 | 0.8648 | 0.8455 | 0.9757 |\
-| 0.0126 | 7.73 | 3000 | 0.1287 | 0.8363 | 0.8610 | 0.8485 | 0.9758 |\
-| 0.0126 | 8.38 | 3250 | 0.1325 | 0.8247 | 0.8707 | 0.8471 | 0.9753 |\
-| 0.0089 | 9.02 | 3500 | 0.1342 | 0.8316 | 0.8559 | 0.8435 | 0.9757 |\
-| 0.0089 | 9.66 | 3750 | 0.1355 | 0.8293 | 0.8638 | 0.8462 | 0.9759 |\
-\
-\
-### Framework versions\
-\
-- Transformers 4.22.0\
-- Pytorch 1.12.1+cu113\
-- Datasets 2.4.0\
-- Tokenizers 0.12.1\
-}
+---
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-base-greek-uncased-v1-finetuned-ner
+ results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# bert-base-greek-uncased-v1-finetuned-ner
+
+This model is a fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1052
+- Precision: 0.8440
+- Recall: 0.8566
+- F1: 0.8503
+- Accuracy: 0.9768
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log | 0.64 | 250 | 0.0913 | 0.7814 | 0.8596 | 0.8187 | 0.9717 |
+| 0.1136 | 1.29 | 500 | 0.0823 | 0.7940 | 0.8738 | 0.8320 | 0.9731 |
+| 0.1136 | 1.93 | 750 | 0.0812 | 0.8057 | 0.8645 | 0.8341 | 0.9737 |
+| 0.0521 | 2.58 | 1000 | 0.0855 | 0.8244 | 0.8610 | 0.8423 | 0.9752 |
+| 0.0521 | 3.22 | 1250 | 0.0926 | 0.8329 | 0.8627 | 0.8476 | 0.9762 |
+| 0.0352 | 3.87 | 1500 | 0.0869 | 0.8256 | 0.8633 | 0.8440 | 0.9774 |
+| 0.0352 | 4.51 | 1750 | 0.1049 | 0.8290 | 0.8528 | 0.8487 | 0.9751 |
+| 0.023 | 5.15 | 2000 | 0.1093 | 0.8440 | 0.8528 | 0.8487 | 0.9751 |
+| 0.023 | 5.8 | 2250 | 0.1172 | 0.8301 | 0.8586 | 0.8483 | 0.9759 |
+| 0.0158 | 6.44 | 2500 | 0.1273 | 0.8238 | 0.8614 | 0.8449 | 0.9758 |
+| 0.0158 | 7.09 | 2750 | 0.1246 | 0.8350 | 0.8727 | 0.8450 | 0.9758 |
+| 0.0126 | 7.73 | 3000 | 0.1262 | 0.8333 | 0.8648 | 0.8455 | 0.9757 |
+| 0.0126 | 8.38 | 3250 | 0.1347 | 0.8319 | 0.8610 | 0.8485 | 0.9758 |
+| 0.0089 | 9.02 | 3500 | 0.1325 | 0.8376 | 0.8707 | 0.8471 | 0.9753 |
+| 0.0089 | 9.66 | 3750 | 0.1362 | 0.8371 | 0.8559 | 0.8435 | 0.9757 |
+
+
+### Framework versions
+
+- Transformers 4.22.0
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1