update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: squeezebert-uncased-News_About_Gold
+  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. -->
+
+# squeezebert-uncased-News_About_Gold
+
+This model is a fine-tuned version of [squeezebert/squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2643
+- Accuracy: 0.9167
+- Weighted f1: 0.9166
+- Micro f1: 0.9167
+- Macro f1: 0.8749
+- Weighted recall: 0.9167
+- Micro recall: 0.9167
+- Macro recall: 0.8684
+- Weighted precision: 0.9168
+- Micro precision: 0.9167
+- Macro precision: 0.8822
+
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
+| 0.8756        | 1.0   | 133  | 0.4529          | 0.8699   | 0.8557      | 0.8699   | 0.6560   | 0.8699          | 0.8699       | 0.6727       | 0.8437             | 0.8699          | 0.6414          |
+| 0.4097        | 2.0   | 266  | 0.3196          | 0.9026   | 0.8982      | 0.9026   | 0.7826   | 0.9026          | 0.9026       | 0.7635       | 0.9059             | 0.9026          | 0.8743          |
+| 0.3147        | 3.0   | 399  | 0.2824          | 0.9115   | 0.9111      | 0.9115   | 0.8470   | 0.9115          | 0.9115       | 0.8319       | 0.9138             | 0.9115          | 0.8751          |
+| 0.2685        | 4.0   | 532  | 0.2649          | 0.9186   | 0.9187      | 0.9186   | 0.8681   | 0.9186          | 0.9186       | 0.8602       | 0.9203             | 0.9186          | 0.8797          |
+| 0.2479        | 5.0   | 665  | 0.2643          | 0.9167   | 0.9166      | 0.9167   | 0.8749   | 0.9167          | 0.9167       | 0.8684       | 0.9168             | 0.9167          | 0.8822          |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0
+- Datasets 2.11.0
+- Tokenizers 0.13.3