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-product
+  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: 0.8690996641956535
+    - name: F1
+      type: f1
+      value: 0.8645310918904254
+    - name: Recall
+      type: recall
+      value: 0.8690996641956535
+    - name: Precision
+      type: precision
+      value: 0.8629318199420283
+---
+
+<!-- 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-product
+
+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: 0.4431
+- Accuracy: 0.8691
+- F1: 0.8645
+- Recall: 0.8691
+- Precision: 0.8629
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.562         | 0.51  | 2000  | 0.5107          | 0.8452   | 0.8346 | 0.8452 | 0.8252    |
+| 0.4548        | 1.01  | 4000  | 0.4628          | 0.8565   | 0.8481 | 0.8565 | 0.8466    |
+| 0.3439        | 1.52  | 6000  | 0.4519          | 0.8605   | 0.8544 | 0.8605 | 0.8545    |
+| 0.2626        | 2.03  | 8000  | 0.4412          | 0.8678   | 0.8618 | 0.8678 | 0.8626    |
+| 0.2717        | 2.53  | 10000 | 0.4431          | 0.8691   | 0.8645 | 0.8691 | 0.8629    |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.11.0+cu113
+- Datasets 2.3.2
+- Tokenizers 0.12.1