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---
library_name: transformers
license: mit
base_model: xlnet-large-cased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: CS221-xlnet-large-cased-finetuned-augmentation
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. -->
# CS221-xlnet-large-cased-finetuned-augmentation
This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2505
- F1: 0.9280
- Roc Auc: 0.9437
- Accuracy: 0.8749
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.5536 | 1.0 | 360 | 0.5670 | 0.1475 | 0.5 | 0.1425 |
| 0.4339 | 2.0 | 720 | 0.4273 | 0.5310 | 0.6840 | 0.3391 |
| 0.3421 | 3.0 | 1080 | 0.3298 | 0.7397 | 0.8147 | 0.4927 |
| 0.2113 | 4.0 | 1440 | 0.2764 | 0.8083 | 0.8578 | 0.6338 |
| 0.1513 | 5.0 | 1800 | 0.2489 | 0.8476 | 0.8869 | 0.6810 |
| 0.1097 | 6.0 | 2160 | 0.2260 | 0.8777 | 0.9021 | 0.7665 |
| 0.0735 | 7.0 | 2520 | 0.2448 | 0.8996 | 0.9200 | 0.8026 |
| 0.0411 | 8.0 | 2880 | 0.2378 | 0.9129 | 0.9315 | 0.8214 |
| 0.0468 | 9.0 | 3240 | 0.2554 | 0.8990 | 0.9316 | 0.8235 |
| 0.0253 | 10.0 | 3600 | 0.2470 | 0.9084 | 0.9306 | 0.8464 |
| 0.021 | 11.0 | 3960 | 0.2412 | 0.9183 | 0.9388 | 0.8582 |
| 0.0125 | 12.0 | 4320 | 0.2440 | 0.9217 | 0.9383 | 0.8631 |
| 0.002 | 13.0 | 4680 | 0.2552 | 0.9232 | 0.9391 | 0.8673 |
| 0.0038 | 14.0 | 5040 | 0.2489 | 0.9237 | 0.9412 | 0.8666 |
| 0.0064 | 15.0 | 5400 | 0.2522 | 0.9234 | 0.9407 | 0.8659 |
| 0.0017 | 16.0 | 5760 | 0.2466 | 0.9254 | 0.9410 | 0.8728 |
| 0.0009 | 17.0 | 6120 | 0.2505 | 0.9280 | 0.9437 | 0.8749 |
| 0.0013 | 18.0 | 6480 | 0.2529 | 0.9272 | 0.9420 | 0.8749 |
| 0.002 | 19.0 | 6840 | 0.2530 | 0.9258 | 0.9412 | 0.8721 |
| 0.001 | 20.0 | 7200 | 0.2530 | 0.9258 | 0.9412 | 0.8721 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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