End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- generator
+metrics:
+- recall
+- precision
+- accuracy
+model-index:
+- name: distilbert-sql-timeout-classifier-with-trained-tokenizer
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: generator
+      type: generator
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Recall
+      type: recall
+      value: 0.7370441458733206
+    - name: Precision
+      type: precision
+      value: 0.15262321144674085
+    - name: Accuracy
+      type: accuracy
+      value: 0.8761327655857626
+---
+
+<!-- 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-sql-timeout-classifier-with-trained-tokenizer
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the generator dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4898
+- Recall: 0.7370
+- Precision: 0.1526
+- Affect Rate: 0.1164
+- Accuracy: 0.8761
+
+## 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: 4
+- eval_batch_size: 4
+- 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 | Recall | Precision | Affect Rate | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:-----------:|:--------:|
+| 0.5018        | 1.0   | 1946 | 0.3744          | 0.6929 | 0.1758    | 0.0924      | 0.8988   |
+| 0.3196        | 2.0   | 3892 | 0.4938          | 0.7390 | 0.1294    | 0.1414      | 0.8512   |
+| 0.2219        | 3.0   | 5838 | 0.4898          | 0.7370 | 0.1526    | 0.1164      | 0.8761   |
+
+
+### Framework versions
+
+- Transformers 4.38.1
+- Pytorch 2.2.1+cu121
+- Datasets 2.17.1
+- Tokenizers 0.15.2

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