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-2
+  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-2
+
+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: 0.2779
+- Accuracy: 0.9421
+- Precision Weighted: 0.9429
+- Recall Weighted: 0.9421
+- F1 Weighted: 0.9421
+- Precision Macro: 0.9429
+- Recall Macro: 0.9421
+- F1 Macro: 0.9421
+
+## 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   | 48   | 0.2252          | 0.9144   | 0.9144             | 0.9144          | 0.9144      | 0.9144          | 0.9144       | 0.9144   |
+| No log        | 2.0   | 96   | 0.1682          | 0.9329   | 0.9333             | 0.9329          | 0.9329      | 0.9333          | 0.9329       | 0.9329   |
+| No log        | 3.0   | 144  | 0.2251          | 0.9236   | 0.9269             | 0.9236          | 0.9235      | 0.9269          | 0.9236       | 0.9235   |
+| No log        | 4.0   | 192  | 0.2421          | 0.9352   | 0.9376             | 0.9352          | 0.9351      | 0.9376          | 0.9352       | 0.9351   |
+| No log        | 5.0   | 240  | 0.2138          | 0.9375   | 0.9383             | 0.9375          | 0.9375      | 0.9383          | 0.9375       | 0.9375   |
+| No log        | 6.0   | 288  | 0.2165          | 0.9398   | 0.9399             | 0.9398          | 0.9398      | 0.9399          | 0.9398       | 0.9398   |
+| No log        | 7.0   | 336  | 0.2470          | 0.9398   | 0.9408             | 0.9398          | 0.9398      | 0.9408          | 0.9398       | 0.9398   |
+| No log        | 8.0   | 384  | 0.2509          | 0.9352   | 0.9353             | 0.9352          | 0.9352      | 0.9353          | 0.9352       | 0.9352   |
+| No log        | 9.0   | 432  | 0.2686          | 0.9352   | 0.9355             | 0.9352          | 0.9352      | 0.9355          | 0.9352       | 0.9352   |
+| No log        | 10.0  | 480  | 0.2779          | 0.9421   | 0.9429             | 0.9421          | 0.9421      | 0.9429          | 0.9421       | 0.9421   |
+
+
+### Framework versions
+
+- Transformers 4.43.3
+- Pytorch 2.3.1
+- Datasets 2.20.0
+- Tokenizers 0.19.1