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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilBERT_without_preprocessing_grid_search
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_without_preprocessing_grid_search
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: 0.6731
- Precision: 0.8400
- Recall: 0.8427
- F1: 0.8407
- Accuracy: 0.8779
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 257 | 0.6542 | 0.7446 | 0.8052 | 0.7657 | 0.8350 |
| 0.8635 | 2.0 | 514 | 0.5548 | 0.7961 | 0.8277 | 0.8056 | 0.8540 |
| 0.8635 | 3.0 | 771 | 0.4839 | 0.7912 | 0.8427 | 0.8115 | 0.8589 |
| 0.3097 | 4.0 | 1028 | 0.5256 | 0.8148 | 0.8544 | 0.8315 | 0.8667 |
| 0.3097 | 5.0 | 1285 | 0.5657 | 0.8346 | 0.8494 | 0.8413 | 0.8764 |
| 0.1839 | 6.0 | 1542 | 0.6005 | 0.8208 | 0.8430 | 0.8304 | 0.8710 |
| 0.1839 | 7.0 | 1799 | 0.6580 | 0.8319 | 0.8349 | 0.8314 | 0.8706 |
| 0.1254 | 8.0 | 2056 | 0.6348 | 0.8342 | 0.8515 | 0.8423 | 0.8774 |
| 0.1254 | 9.0 | 2313 | 0.6601 | 0.8314 | 0.8394 | 0.8348 | 0.8745 |
| 0.0935 | 10.0 | 2570 | 0.6731 | 0.8400 | 0.8427 | 0.8407 | 0.8779 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|