update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: aesthetic_attribute_classifier
+  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. -->
+
+# aesthetic_attribute_classifier
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3976
+- Precision: {'precision': 0.877129341279301}
+- Recall: {'recall': 0.8751381215469614}
+- F1: {'f1': 0.875529982855803}
+- Accuracy: {'accuracy': 0.8751381215469614}
+
+## 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision                         | Recall                         | F1                         | Accuracy                         |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:------------------------------:|:--------------------------:|:--------------------------------:|
+| 0.452         | 1.0   | 1528 | 0.4109          | {'precision': 0.8632779077963935} | {'recall': 0.8615101289134438} | {'f1': 0.8618616182904953} | {'accuracy': 0.8615101289134438} |
+| 0.3099        | 2.0   | 3056 | 0.3976          | {'precision': 0.877129341279301}  | {'recall': 0.8751381215469614} | {'f1': 0.875529982855803}  | {'accuracy': 0.8751381215469614} |
+| 0.227         | 3.0   | 4584 | 0.4320          | {'precision': 0.876211408446225}  | {'recall': 0.874401473296501}  | {'f1': 0.8747427955387239} | {'accuracy': 0.874401473296501}  |
+| 0.1645        | 4.0   | 6112 | 0.4840          | {'precision': 0.8724641667216837} | {'recall': 0.8714548802946593} | {'f1': 0.8714577820909117} | {'accuracy': 0.8714548802946593} |
+| 0.1141        | 5.0   | 7640 | 0.5083          | {'precision': 0.8755445355051571} | {'recall': 0.8747697974217311} | {'f1': 0.8748766125899489} | {'accuracy': 0.8747697974217311} |
+
+
+### Framework versions
+
+- Transformers 4.16.2
+- Pytorch 1.10.2+cu113
+- Datasets 1.18.3
+- Tokenizers 0.11.0