---
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
  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-semeval

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: 0.6440
- F1: 0.7846
- Roc Auc: 0.8368
- Accuracy: 0.4892

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.577         | 1.0   | 139  | 0.5832          | 0.4593 | 0.6262  | 0.1516   |
| 0.5274        | 2.0   | 278  | 0.5171          | 0.4943 | 0.6506  | 0.1805   |
| 0.429         | 3.0   | 417  | 0.3725          | 0.7256 | 0.7927  | 0.4188   |
| 0.3146        | 4.0   | 556  | 0.3735          | 0.7468 | 0.8089  | 0.4567   |
| 0.2192        | 5.0   | 695  | 0.3846          | 0.7625 | 0.8216  | 0.4729   |
| 0.167         | 6.0   | 834  | 0.4137          | 0.7541 | 0.8126  | 0.4585   |
| 0.0954        | 7.0   | 973  | 0.4414          | 0.7672 | 0.8222  | 0.4783   |
| 0.0668        | 8.0   | 1112 | 0.5105          | 0.7696 | 0.8271  | 0.4747   |
| 0.0299        | 9.0   | 1251 | 0.5573          | 0.7688 | 0.8263  | 0.4531   |
| 0.0258        | 10.0  | 1390 | 0.5910          | 0.7793 | 0.8366  | 0.4783   |
| 0.0132        | 11.0  | 1529 | 0.6008          | 0.7741 | 0.8298  | 0.4801   |
| 0.0082        | 12.0  | 1668 | 0.6108          | 0.7780 | 0.8340  | 0.4711   |
| 0.0054        | 13.0  | 1807 | 0.6386          | 0.7806 | 0.8356  | 0.4711   |
| 0.0033        | 14.0  | 1946 | 0.6429          | 0.7775 | 0.8325  | 0.4747   |
| 0.0025        | 15.0  | 2085 | 0.6464          | 0.7763 | 0.8314  | 0.4675   |
| 0.0028        | 16.0  | 2224 | 0.6440          | 0.7846 | 0.8368  | 0.4892   |
| 0.0029        | 17.0  | 2363 | 0.6455          | 0.7816 | 0.8344  | 0.4856   |
| 0.0029        | 18.0  | 2502 | 0.6496          | 0.7777 | 0.8316  | 0.4765   |
| 0.0023        | 19.0  | 2641 | 0.6500          | 0.7812 | 0.8347  | 0.4819   |


### Framework versions

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