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-complaints-wandb
+  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: 0.868877906608376
+    - name: F1
+      type: f1
+      value: 0.8630522401242867
+    - name: Recall
+      type: recall
+      value: 0.868877906608376
+    - name: Precision
+      type: precision
+      value: 0.8616053523512515
+---
+
+<!-- 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-complaints-wandb
+
+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: 0.4448
+- Accuracy: 0.8689
+- F1: 0.8631
+- Recall: 0.8689
+- Precision: 0.8616
+
+## 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: 500
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Recall | Precision |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.571         | 0.51  | 2000  | 0.5150          | 0.8469   | 0.8349 | 0.8469 | 0.8249    |
+| 0.4765        | 1.01  | 4000  | 0.4676          | 0.8561   | 0.8451 | 0.8561 | 0.8376    |
+| 0.3376        | 1.52  | 6000  | 0.4560          | 0.8609   | 0.8546 | 0.8609 | 0.8547    |
+| 0.268         | 2.03  | 8000  | 0.4399          | 0.8684   | 0.8611 | 0.8684 | 0.8607    |
+| 0.2654        | 2.53  | 10000 | 0.4448          | 0.8689   | 0.8631 | 0.8689 | 0.8616    |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.11.0+cu113
+- Datasets 2.3.2
+- Tokenizers 0.12.1