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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-uncased_11112024T103209
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-base-uncased_11112024T103209
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: 0.4280
- F1: 0.8712
- Learning Rate: 0.0
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 600
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Rate |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| No log | 0.9942 | 86 | 1.7522 | 0.1396 | 0.0000 |
| No log | 2.0 | 173 | 1.5504 | 0.3793 | 0.0000 |
| No log | 2.9942 | 259 | 1.3168 | 0.5063 | 0.0000 |
| No log | 4.0 | 346 | 1.0578 | 0.5762 | 0.0000 |
| No log | 4.9942 | 432 | 0.8963 | 0.6332 | 0.0000 |
| 1.3438 | 6.0 | 519 | 0.7904 | 0.6792 | 0.0000 |
| 1.3438 | 6.9942 | 605 | 0.6959 | 0.7280 | 2e-05 |
| 1.3438 | 8.0 | 692 | 0.5408 | 0.8100 | 2e-05 |
| 1.3438 | 8.9942 | 778 | 0.4754 | 0.8469 | 0.0000 |
| 1.3438 | 10.0 | 865 | 0.4280 | 0.8712 | 0.0000 |
| 1.3438 | 10.9942 | 951 | 0.4683 | 0.8750 | 0.0000 |
| 0.4057 | 12.0 | 1038 | 0.5107 | 0.8769 | 0.0000 |
| 0.4057 | 12.9942 | 1124 | 0.5242 | 0.8879 | 0.0000 |
| 0.4057 | 14.0 | 1211 | 0.6143 | 0.8807 | 0.0000 |
| 0.4057 | 14.9942 | 1297 | 0.6044 | 0.8844 | 0.0000 |
| 0.4057 | 16.0 | 1384 | 0.5825 | 0.8942 | 0.0000 |
| 0.4057 | 16.9942 | 1470 | 0.6377 | 0.8896 | 0.0000 |
| 0.0457 | 18.0 | 1557 | 0.7469 | 0.8774 | 0.0000 |
| 0.0457 | 18.9942 | 1643 | 0.7769 | 0.8818 | 0.0000 |
| 0.0457 | 20.0 | 1730 | 0.6606 | 0.8943 | 0.0000 |
| 0.0457 | 20.9942 | 1816 | 0.7124 | 0.8915 | 0.0000 |
| 0.0457 | 22.0 | 1903 | 0.7385 | 0.8879 | 0.0000 |
| 0.0457 | 22.9942 | 1989 | 0.6596 | 0.8977 | 0.0000 |
| 0.0106 | 24.0 | 2076 | 0.7477 | 0.8887 | 0.0000 |
| 0.0106 | 24.9942 | 2162 | 0.6636 | 0.8990 | 0.0000 |
| 0.0106 | 26.0 | 2249 | 0.7530 | 0.8924 | 0.0000 |
| 0.0106 | 26.9942 | 2335 | 0.7221 | 0.8944 | 0.0000 |
| 0.0106 | 28.0 | 2422 | 0.7504 | 0.8931 | 0.0000 |
| 0.0051 | 28.9942 | 2508 | 0.7383 | 0.8951 | 0.0000 |
| 0.0051 | 30.0 | 2595 | 0.7678 | 0.8904 | 0.0000 |
| 0.0051 | 30.9942 | 2681 | 0.7626 | 0.8903 | 0.0000 |
| 0.0051 | 32.0 | 2768 | 0.7509 | 0.8915 | 0.0000 |
| 0.0051 | 32.9942 | 2854 | 0.7659 | 0.8915 | 2e-06 |
| 0.0051 | 34.0 | 2941 | 0.7721 | 0.8905 | 0.0000 |
| 0.0032 | 34.9942 | 3027 | 0.7705 | 0.8904 | 1e-06 |
| 0.0032 | 36.0 | 3114 | 0.7724 | 0.8893 | 7e-07 |
| 0.0032 | 36.9942 | 3200 | 0.7740 | 0.8895 | 4e-07 |
| 0.0032 | 38.0 | 3287 | 0.7749 | 0.8892 | 1e-07 |
| 0.0032 | 38.9942 | 3373 | 0.7746 | 0.8889 | 0.0 |
| 0.0032 | 39.7688 | 3440 | 0.7747 | 0.8889 | 0.0 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.19.1
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