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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_finetune_own_data_model
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_finetune_own_data_model
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: 0.2715
- Precision: 0.8333
- Recall: 0.8333
- F1: 0.8333
- Accuracy: 0.9483
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 7 | 0.6895 | 0.0 | 0.0 | 0.0 | 0.7241 |
| No log | 2.0 | 14 | 0.5915 | 0.0 | 0.0 | 0.0 | 0.7241 |
| No log | 3.0 | 21 | 0.4062 | 0.2 | 0.0833 | 0.1176 | 0.7759 |
| No log | 4.0 | 28 | 0.3063 | 0.5 | 0.5833 | 0.5385 | 0.8966 |
| No log | 5.0 | 35 | 0.2520 | 0.5333 | 0.6667 | 0.5926 | 0.9138 |
| No log | 6.0 | 42 | 0.2474 | 0.6667 | 0.6667 | 0.6667 | 0.9310 |
| No log | 7.0 | 49 | 0.2140 | 0.6923 | 0.75 | 0.7200 | 0.9483 |
| No log | 8.0 | 56 | 0.1894 | 0.8333 | 0.8333 | 0.8333 | 0.9655 |
| No log | 9.0 | 63 | 0.1890 | 0.8333 | 0.8333 | 0.8333 | 0.9655 |
| No log | 10.0 | 70 | 0.2119 | 0.8182 | 0.75 | 0.7826 | 0.9483 |
| No log | 11.0 | 77 | 0.2343 | 0.8182 | 0.75 | 0.7826 | 0.9483 |
| No log | 12.0 | 84 | 0.2421 | 0.8182 | 0.75 | 0.7826 | 0.9483 |
| No log | 13.0 | 91 | 0.2379 | 0.8182 | 0.75 | 0.7826 | 0.9483 |
| No log | 14.0 | 98 | 0.2362 | 0.8182 | 0.75 | 0.7826 | 0.9483 |
| No log | 15.0 | 105 | 0.2357 | 0.8182 | 0.75 | 0.7826 | 0.9483 |
| No log | 16.0 | 112 | 0.2370 | 0.8182 | 0.75 | 0.7826 | 0.9483 |
| No log | 17.0 | 119 | 0.2383 | 0.8182 | 0.75 | 0.7826 | 0.9483 |
| No log | 18.0 | 126 | 0.2400 | 0.8182 | 0.75 | 0.7826 | 0.9483 |
| No log | 19.0 | 133 | 0.2424 | 0.8182 | 0.75 | 0.7826 | 0.9483 |
| No log | 20.0 | 140 | 0.2444 | 0.8182 | 0.75 | 0.7826 | 0.9483 |
| No log | 21.0 | 147 | 0.2461 | 0.8333 | 0.8333 | 0.8333 | 0.9655 |
| No log | 22.0 | 154 | 0.2481 | 0.8333 | 0.8333 | 0.8333 | 0.9655 |
| No log | 23.0 | 161 | 0.2422 | 0.8333 | 0.8333 | 0.8333 | 0.9655 |
| No log | 24.0 | 168 | 0.2408 | 0.8333 | 0.8333 | 0.8333 | 0.9655 |
| No log | 25.0 | 175 | 0.2418 | 0.8333 | 0.8333 | 0.8333 | 0.9655 |
| No log | 26.0 | 182 | 0.2444 | 0.8333 | 0.8333 | 0.8333 | 0.9655 |
| No log | 27.0 | 189 | 0.2477 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 28.0 | 196 | 0.2504 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 29.0 | 203 | 0.2527 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 30.0 | 210 | 0.2545 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 31.0 | 217 | 0.2561 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 32.0 | 224 | 0.2572 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 33.0 | 231 | 0.2584 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 34.0 | 238 | 0.2596 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 35.0 | 245 | 0.2606 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 36.0 | 252 | 0.2613 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 37.0 | 259 | 0.2621 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 38.0 | 266 | 0.2629 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 39.0 | 273 | 0.2638 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 40.0 | 280 | 0.2645 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 41.0 | 287 | 0.2652 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 42.0 | 294 | 0.2659 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 43.0 | 301 | 0.2666 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 44.0 | 308 | 0.2672 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 45.0 | 315 | 0.2678 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 46.0 | 322 | 0.2683 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 47.0 | 329 | 0.2686 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 48.0 | 336 | 0.2689 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 49.0 | 343 | 0.2693 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 50.0 | 350 | 0.2697 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 51.0 | 357 | 0.2699 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 52.0 | 364 | 0.2702 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 53.0 | 371 | 0.2705 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 54.0 | 378 | 0.2708 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 55.0 | 385 | 0.2710 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 56.0 | 392 | 0.2711 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 57.0 | 399 | 0.2713 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 58.0 | 406 | 0.2714 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 59.0 | 413 | 0.2715 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
| No log | 60.0 | 420 | 0.2715 | 0.8333 | 0.8333 | 0.8333 | 0.9483 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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