End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: distilbert-q-classifier-3
+  results: []
+---
+
+<!-- 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-q-classifier-3
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3192
+- Accuracy: 0.9238
+- Precision Weighted: 0.9240
+- Recall Weighted: 0.9238
+- F1 Weighted: 0.9239
+- Precision Macro: 0.9240
+- Recall Macro: 0.9241
+- F1 Macro: 0.9240
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Weighted | Recall Weighted | F1 Weighted | Precision Macro | Recall Macro | F1 Macro |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------------------:|:---------------:|:-----------:|:---------------:|:------------:|:--------:|
+| No log        | 1.0   | 68   | 0.4096          | 0.8558   | 0.8587             | 0.8558          | 0.8567      | 0.8588          | 0.8561       | 0.8569   |
+| No log        | 2.0   | 136  | 0.3029          | 0.8963   | 0.8959             | 0.8963          | 0.8959      | 0.8965          | 0.8964       | 0.8962   |
+| No log        | 3.0   | 204  | 0.2803          | 0.8914   | 0.8935             | 0.8914          | 0.8898      | 0.8942          | 0.8911       | 0.8900   |
+| No log        | 4.0   | 272  | 0.2651          | 0.9109   | 0.9132             | 0.9109          | 0.9114      | 0.9135          | 0.9105       | 0.9113   |
+| No log        | 5.0   | 340  | 0.2840          | 0.9222   | 0.9247             | 0.9222          | 0.9226      | 0.9241          | 0.9231       | 0.9228   |
+| No log        | 6.0   | 408  | 0.2939          | 0.9254   | 0.9253             | 0.9254          | 0.9253      | 0.9252          | 0.9258       | 0.9254   |
+| No log        | 7.0   | 476  | 0.3011          | 0.9238   | 0.9242             | 0.9238          | 0.9239      | 0.9241          | 0.9242       | 0.9241   |
+| 0.2181        | 8.0   | 544  | 0.3170          | 0.9190   | 0.9199             | 0.9190          | 0.9192      | 0.9201          | 0.9186       | 0.9191   |
+| 0.2181        | 9.0   | 612  | 0.3135          | 0.9222   | 0.9224             | 0.9222          | 0.9223      | 0.9225          | 0.9220       | 0.9223   |
+| 0.2181        | 10.0  | 680  | 0.3192          | 0.9238   | 0.9240             | 0.9238          | 0.9239      | 0.9240          | 0.9241       | 0.9240   |
+
+
+### Framework versions
+
+- Transformers 4.43.3
+- Pytorch 2.3.1
+- Datasets 2.20.0
+- Tokenizers 0.19.1