End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: google-bert/bert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: BERT_ST_DA_1000
+  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. -->
+
+# BERT_ST_DA_1000
+
+This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1864
+- Precision: 0.9653
+- Recall: 0.9736
+- F1: 0.9694
+- Accuracy: 0.9655
+
+## 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 495  | 0.1375          | 0.9614    | 0.9662 | 0.9638 | 0.9590   |
+| 0.2452        | 2.0   | 990  | 0.1262          | 0.9652    | 0.9705 | 0.9679 | 0.9632   |
+| 0.0853        | 3.0   | 1485 | 0.1396          | 0.9638    | 0.9677 | 0.9657 | 0.9619   |
+| 0.0479        | 4.0   | 1980 | 0.1485          | 0.9637    | 0.9729 | 0.9683 | 0.9651   |
+| 0.0275        | 5.0   | 2475 | 0.1641          | 0.9633    | 0.9727 | 0.9680 | 0.9642   |
+| 0.0181        | 6.0   | 2970 | 0.1753          | 0.9641    | 0.9739 | 0.9689 | 0.9655   |
+| 0.0112        | 7.0   | 3465 | 0.1675          | 0.9659    | 0.9724 | 0.9691 | 0.9657   |
+| 0.0074        | 8.0   | 3960 | 0.1817          | 0.9650    | 0.9745 | 0.9697 | 0.9663   |
+| 0.0054        | 9.0   | 4455 | 0.1878          | 0.9652    | 0.9737 | 0.9694 | 0.9657   |
+| 0.0038        | 10.0  | 4950 | 0.1864          | 0.9653    | 0.9736 | 0.9694 | 0.9655   |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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