End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- rotten_tomatoes
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: my_distilbert_model
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: rotten_tomatoes
+      type: rotten_tomatoes
+      config: default
+      split: test
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8433395872420263
+    - name: F1
+      type: f1
+      value: 0.8432898032121621
+    - name: Precision
+      type: precision
+      value: 0.843776433767552
+    - name: Recall
+      type: recall
+      value: 0.8433395872420262
+---
+
+<!-- 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. -->
+
+# my_distilbert_model
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5593
+- Accuracy: 0.8433
+- F1: 0.8433
+- Precision: 0.8438
+- Recall: 0.8433
+
+## 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: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.4222        | 1.0   | 534  | 0.3821          | 0.8424   | 0.8421 | 0.8450    | 0.8424 |
+| 0.2558        | 2.0   | 1068 | 0.4620          | 0.8433   | 0.8432 | 0.8445    | 0.8433 |
+| 0.1609        | 3.0   | 1602 | 0.5593          | 0.8433   | 0.8433 | 0.8438    | 0.8433 |
+
+
+### Framework versions
+
+- Transformers 4.34.1
+- Pytorch 2.1.0
+- Datasets 2.14.6
+- Tokenizers 0.14.1

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