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---
license: apache-2.0
base_model: google-bert/bert-large-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: cfd_model2
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. -->
# cfd_model2
This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0066
- Precision: 0.9978
- Recall: 0.9987
- F1: 0.9982
- Accuracy: 0.9986
## 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: 3e-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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0493 | 0.19 | 100 | 0.0352 | 0.9890 | 0.9930 | 0.9910 | 0.9937 |
| 0.0103 | 0.39 | 200 | 0.0273 | 0.9886 | 0.9956 | 0.9921 | 0.9946 |
| 0.0011 | 0.58 | 300 | 0.0190 | 0.9921 | 0.9978 | 0.9950 | 0.9960 |
| 0.0247 | 0.77 | 400 | 0.0167 | 0.9978 | 0.9943 | 0.9960 | 0.9968 |
| 0.0003 | 0.97 | 500 | 0.0269 | 0.9926 | 0.9978 | 0.9952 | 0.9961 |
| 0.0036 | 1.16 | 600 | 0.0133 | 0.9960 | 0.9960 | 0.9960 | 0.9968 |
| 0.0008 | 1.35 | 700 | 0.0222 | 0.9926 | 0.9987 | 0.9956 | 0.9965 |
| 0.0003 | 1.55 | 800 | 0.0287 | 0.9895 | 0.9974 | 0.9934 | 0.9953 |
| 0.0005 | 1.74 | 900 | 0.0132 | 0.9934 | 0.9982 | 0.9958 | 0.9970 |
| 0.0024 | 1.93 | 1000 | 0.0123 | 0.9952 | 0.9982 | 0.9967 | 0.9977 |
| 0.0007 | 2.13 | 1100 | 0.0099 | 0.9969 | 0.9943 | 0.9956 | 0.9965 |
| 0.0005 | 2.32 | 1200 | 0.0087 | 0.9978 | 0.9965 | 0.9971 | 0.9977 |
| 0.001 | 2.51 | 1300 | 0.0055 | 0.9974 | 0.9991 | 0.9982 | 0.9986 |
| 0.0002 | 2.71 | 1400 | 0.0049 | 0.9974 | 0.9982 | 0.9978 | 0.9986 |
| 0.0004 | 2.9 | 1500 | 0.0065 | 0.9969 | 0.9982 | 0.9976 | 0.9984 |
| 0.0002 | 3.09 | 1600 | 0.0071 | 0.9969 | 0.9978 | 0.9974 | 0.9982 |
| 0.0001 | 3.29 | 1700 | 0.0077 | 0.9974 | 0.9978 | 0.9976 | 0.9984 |
| 0.0002 | 3.48 | 1800 | 0.0072 | 0.9974 | 0.9978 | 0.9976 | 0.9984 |
| 0.0005 | 3.68 | 1900 | 0.0072 | 0.9974 | 0.9978 | 0.9976 | 0.9984 |
| 0.0207 | 3.87 | 2000 | 0.0066 | 0.9978 | 0.9987 | 0.9982 | 0.9986 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
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