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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: distil-bert-imeoocap
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. -->
# distil-bert-imeoocap
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: 1.5520
- F1: 0.6416
- Precision: 0.6437
- Recall: 0.6442
- Accuracy: 0.6442
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.4777 | 1.0 | 74 | 1.1554 | 0.6498 | 0.6489 | 0.6538 | 0.6538 |
| 0.395 | 2.0 | 148 | 1.2060 | 0.6062 | 0.6109 | 0.6135 | 0.6135 |
| 0.364 | 3.0 | 222 | 1.2625 | 0.6329 | 0.6436 | 0.6423 | 0.6423 |
| 0.3402 | 4.0 | 296 | 1.3512 | 0.6247 | 0.6330 | 0.6269 | 0.6269 |
| 0.3135 | 5.0 | 370 | 1.3587 | 0.6472 | 0.6442 | 0.6519 | 0.6519 |
| 0.307 | 6.0 | 444 | 1.4376 | 0.6258 | 0.6334 | 0.6288 | 0.6288 |
| 0.2903 | 7.0 | 518 | 1.3565 | 0.6502 | 0.6550 | 0.65 | 0.65 |
| 0.2931 | 8.0 | 592 | 1.4059 | 0.6310 | 0.6273 | 0.6365 | 0.6365 |
| 0.2805 | 9.0 | 666 | 1.3972 | 0.6357 | 0.6370 | 0.6365 | 0.6365 |
| 0.2772 | 10.0 | 740 | 1.4938 | 0.6205 | 0.6204 | 0.6308 | 0.6308 |
| 0.2767 | 11.0 | 814 | 1.4324 | 0.6256 | 0.6324 | 0.6269 | 0.6269 |
| 0.2634 | 12.0 | 888 | 1.5399 | 0.6457 | 0.6487 | 0.65 | 0.65 |
| 0.2829 | 13.0 | 962 | 1.4857 | 0.6369 | 0.6363 | 0.6385 | 0.6385 |
| 0.2444 | 14.0 | 1036 | 1.4879 | 0.6314 | 0.6385 | 0.6308 | 0.6308 |
| 0.2424 | 15.0 | 1110 | 1.5049 | 0.6357 | 0.6399 | 0.6365 | 0.6365 |
| 0.2332 | 16.0 | 1184 | 1.5277 | 0.6233 | 0.6268 | 0.625 | 0.625 |
| 0.2215 | 17.0 | 1258 | 1.5550 | 0.6390 | 0.6379 | 0.6423 | 0.6423 |
| 0.2235 | 18.0 | 1332 | 1.5593 | 0.6434 | 0.6449 | 0.6481 | 0.6481 |
| 0.213 | 19.0 | 1406 | 1.5631 | 0.6337 | 0.6303 | 0.6385 | 0.6385 |
| 0.2098 | 20.0 | 1480 | 1.5520 | 0.6416 | 0.6437 | 0.6442 | 0.6442 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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