update model card README.md

README.md

@@ -1,3 +1,69 @@
 ---
-license: mit
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: fine-tuned-bert-base-multilingual-uncased-NED_latest
+  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. -->
+
+# fine-tuned-bert-base-multilingual-uncased-NED_latest
+
+This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1376
+- Accuracy: 0.9687
+- Precision: 0.9761
+- Recall: 0.9764
+- F1: 0.9762
+
+## 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
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step   | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.157         | 1.0   | 56232  | 0.1271          | 0.9658   | 0.9740    | 0.9741 | 0.9740 |
+| 0.144         | 2.0   | 112464 | 0.1549          | 0.9665   | 0.9693    | 0.9802 | 0.9747 |
+| 0.1439        | 3.0   | 168696 | 0.1600          | 0.9660   | 0.9701    | 0.9786 | 0.9743 |
+| 0.1267        | 4.0   | 224928 | 0.1376          | 0.9687   | 0.9761    | 0.9764 | 0.9762 |
+
+
+### Framework versions
+
+- Transformers 4.19.2
+- Pytorch 1.11.0+cu113
+- Datasets 2.2.2
+- Tokenizers 0.12.1