update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- amazon_reviews_multi
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: distilbert-base-uncased-finetuned-amazon-review
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: amazon_reviews_multi
+      type: amazon_reviews_multi
+      args: es
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.693
+    - name: F1
+      type: f1
+      value: 0.7002653469272611
+    - name: Precision
+      type: precision
+      value: 0.709541681233075
+    - name: Recall
+      type: recall
+      value: 0.693
+---
+
+<!-- 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-finetuned-amazon-review
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_reviews_multi dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.3494
+- Accuracy: 0.693
+- F1: 0.7003
+- Precision: 0.7095
+- Recall: 0.693
+
+## 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 | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| No log        | 0.5   | 500  | 0.8287          | 0.7104   | 0.7120 | 0.7152    | 0.7104 |
+| 0.4238        | 1.0   | 1000 | 0.8917          | 0.7094   | 0.6989 | 0.6917    | 0.7094 |
+| 0.4238        | 1.5   | 1500 | 0.9367          | 0.6884   | 0.6983 | 0.7151    | 0.6884 |
+| 0.3152        | 2.0   | 2000 | 0.9845          | 0.7116   | 0.7144 | 0.7176    | 0.7116 |
+| 0.3152        | 2.5   | 2500 | 1.0752          | 0.6814   | 0.6968 | 0.7232    | 0.6814 |
+| 0.2454        | 3.0   | 3000 | 1.1215          | 0.6918   | 0.6954 | 0.7068    | 0.6918 |
+| 0.2454        | 3.5   | 3500 | 1.2905          | 0.6976   | 0.7048 | 0.7138    | 0.6976 |
+| 0.1989        | 4.0   | 4000 | 1.2938          | 0.694    | 0.7016 | 0.7113    | 0.694  |
+| 0.1989        | 4.5   | 4500 | 1.3623          | 0.6972   | 0.7014 | 0.7062    | 0.6972 |
+| 0.1746        | 5.0   | 5000 | 1.3494          | 0.693    | 0.7003 | 0.7095    | 0.693  |
+
+
+### Framework versions
+
+- Transformers 4.15.0
+- Pytorch 1.10.0+cu111
+- Datasets 1.17.0
+- Tokenizers 0.10.3