update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- consumer-finance-complaints
+metrics:
+- accuracy
+- f1
+- recall
+- precision
+model-index:
+- name: distilbert-base-uncased-wandb-week-3-complaints-classifier-1500
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: consumer-finance-complaints
+      type: consumer-finance-complaints
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8219254879975536
+    - name: F1
+      type: f1
+      value: 0.8151998307079064
+    - name: Recall
+      type: recall
+      value: 0.8219254879975536
+    - name: Precision
+      type: precision
+      value: 0.8165753119578384
+---
+
+<!-- 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. -->
+
+# distilbert-base-uncased-wandb-week-3-complaints-classifier-1500
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the consumer-finance-complaints dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5451
+- Accuracy: 0.8219
+- F1: 0.8152
+- Recall: 0.8219
+- Precision: 0.8166
+
+## 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: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 1500
+- num_epochs: 2
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 1.0678        | 0.2   | 500  | 0.9935          | 0.7193   | 0.6715 | 0.7193 | 0.6348    |
+| 0.8447        | 0.41  | 1000 | 0.8331          | 0.7468   | 0.7108 | 0.7468 | 0.6990    |
+| 0.7913        | 0.61  | 1500 | 0.7022          | 0.7770   | 0.7457 | 0.7770 | 0.7685    |
+| 0.6973        | 0.82  | 2000 | 0.6584          | 0.7922   | 0.7710 | 0.7922 | 0.7849    |
+| 0.5572        | 1.02  | 2500 | 0.6034          | 0.8076   | 0.7986 | 0.8076 | 0.7994    |
+| 0.5528        | 1.22  | 3000 | 0.6017          | 0.8085   | 0.7986 | 0.8085 | 0.8063    |
+| 0.5435        | 1.43  | 3500 | 0.5721          | 0.8147   | 0.8085 | 0.8147 | 0.8107    |
+| 0.4995        | 1.63  | 4000 | 0.5598          | 0.8161   | 0.8125 | 0.8161 | 0.8144    |
+| 0.4854        | 1.83  | 4500 | 0.5451          | 0.8219   | 0.8152 | 0.8219 | 0.8166    |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.11.0+cu102
+- Datasets 2.3.2
+- Tokenizers 0.12.1