---
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-semeval-aug
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# CS221-xlnet-large-cased-finetuned-semeval-aug

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: 0.3323
- F1: 0.7655
- Roc Auc: 0.8218
- Accuracy: 0.5483

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.5847        | 1.0   | 277  | 0.5747          | 0.1535 | 0.5011  | 0.1409   |
| 0.5058        | 2.0   | 554  | 0.4907          | 0.3674 | 0.5986  | 0.2367   |
| 0.3991        | 3.0   | 831  | 0.4118          | 0.5551 | 0.6989  | 0.3921   |
| 0.3316        | 4.0   | 1108 | 0.3466          | 0.7102 | 0.7920  | 0.4770   |
| 0.2593        | 5.0   | 1385 | 0.3323          | 0.7655 | 0.8218  | 0.5483   |
| 0.1562        | 6.0   | 1662 | 0.3410          | 0.7838 | 0.8322  | 0.5962   |
| 0.1033        | 7.0   | 1939 | 0.3470          | 0.8023 | 0.8499  | 0.6134   |
| 0.0641        | 8.0   | 2216 | 0.3608          | 0.8102 | 0.8583  | 0.6314   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0