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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: final_V1-distilbert-text-classification-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. -->
# final_V1-distilbert-text-classification-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1494
- Accuracy: 0.9672
- F1: 0.8312
- Precision: 0.8275
- Recall: 0.8357
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.6662 | 0.11 | 50 | 1.6945 | 0.2888 | 0.0820 | 0.1958 | 0.1341 |
| 0.7494 | 0.22 | 100 | 0.6947 | 0.8034 | 0.4962 | 0.4949 | 0.5054 |
| 0.2779 | 0.33 | 150 | 0.4631 | 0.8980 | 0.6685 | 0.6550 | 0.6829 |
| 0.2204 | 0.44 | 200 | 0.3938 | 0.8999 | 0.6686 | 0.6659 | 0.6758 |
| 0.137 | 0.55 | 250 | 0.4153 | 0.9065 | 0.6707 | 0.6537 | 0.6898 |
| 0.1931 | 0.66 | 300 | 0.3093 | 0.9166 | 0.7089 | 0.7728 | 0.7046 |
| 0.1356 | 0.76 | 350 | 0.3384 | 0.9152 | 0.6904 | 0.8123 | 0.6978 |
| 0.1065 | 0.87 | 400 | 0.4172 | 0.9144 | 0.7233 | 0.7804 | 0.7174 |
| 0.105 | 0.98 | 450 | 0.4521 | 0.8852 | 0.7078 | 0.7342 | 0.7051 |
| 0.1275 | 1.09 | 500 | 0.2837 | 0.9262 | 0.7365 | 0.7927 | 0.7275 |
| 0.0754 | 1.2 | 550 | 0.3979 | 0.9180 | 0.7164 | 0.8039 | 0.7133 |
| 0.0861 | 1.31 | 600 | 0.1506 | 0.9604 | 0.8259 | 0.8247 | 0.8280 |
| 0.0514 | 1.42 | 650 | 0.1397 | 0.9664 | 0.8277 | 0.8264 | 0.8293 |
| 0.0536 | 1.53 | 700 | 0.1566 | 0.9642 | 0.8279 | 0.8255 | 0.8308 |
| 0.0351 | 1.64 | 750 | 0.1804 | 0.9620 | 0.8276 | 0.8251 | 0.8312 |
| 0.0862 | 1.75 | 800 | 0.1445 | 0.9655 | 0.8314 | 0.8307 | 0.8322 |
| 0.0461 | 1.86 | 850 | 0.1492 | 0.9669 | 0.8306 | 0.8291 | 0.8324 |
| 0.0663 | 1.97 | 900 | 0.2054 | 0.9604 | 0.8292 | 0.8299 | 0.8295 |
| 0.0482 | 2.07 | 950 | 0.1498 | 0.9655 | 0.8294 | 0.8272 | 0.8324 |
| 0.0299 | 2.18 | 1000 | 0.1657 | 0.9650 | 0.8292 | 0.8269 | 0.8321 |
| 0.0348 | 2.29 | 1050 | 0.1473 | 0.9686 | 0.8310 | 0.8291 | 0.8332 |
| 0.0283 | 2.4 | 1100 | 0.1470 | 0.9694 | 0.8333 | 0.8297 | 0.8376 |
| 0.0115 | 2.51 | 1150 | 0.1496 | 0.9691 | 0.8336 | 0.8317 | 0.8358 |
| 0.004 | 2.62 | 1200 | 0.1671 | 0.9650 | 0.8301 | 0.8280 | 0.8329 |
| 0.0054 | 2.73 | 1250 | 0.1560 | 0.9694 | 0.8333 | 0.8325 | 0.8343 |
| 0.0217 | 2.84 | 1300 | 0.1553 | 0.9696 | 0.8334 | 0.8326 | 0.8345 |
| 0.0054 | 2.95 | 1350 | 0.1603 | 0.9691 | 0.8332 | 0.8324 | 0.8343 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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