---
library_name: transformers
license: mit
base_model: microsoft/deberta-large
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: CS221-deberta-large-finetuned-semeval-NT
  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-deberta-large-finetuned-semeval-NT

This model is a fine-tuned version of [microsoft/deberta-large](https://huggingface.co/microsoft/deberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7626
- F1: 0.7769
- Roc Auc: 0.8347
- Accuracy: 0.4711

## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.3938        | 1.0   | 277  | 0.3549          | 0.7563 | 0.8155  | 0.4585   |
| 0.273         | 2.0   | 554  | 0.3731          | 0.7376 | 0.7993  | 0.4368   |
| 0.1452        | 3.0   | 831  | 0.3962          | 0.7548 | 0.8119  | 0.4729   |
| 0.1046        | 4.0   | 1108 | 0.4750          | 0.7572 | 0.8169  | 0.4495   |
| 0.0635        | 5.0   | 1385 | 0.5714          | 0.7714 | 0.8279  | 0.4621   |
| 0.0512        | 6.0   | 1662 | 0.6294          | 0.7610 | 0.8199  | 0.4567   |
| 0.0605        | 7.0   | 1939 | 0.7023          | 0.7663 | 0.8244  | 0.4639   |
| 0.0122        | 8.0   | 2216 | 0.7376          | 0.7752 | 0.8315  | 0.4765   |
| 0.0083        | 9.0   | 2493 | 0.7626          | 0.7769 | 0.8347  | 0.4711   |
| 0.0041        | 10.0  | 2770 | 0.7788          | 0.7738 | 0.8280  | 0.4856   |
| 0.0018        | 11.0  | 3047 | 0.8313          | 0.7760 | 0.8309  | 0.4910   |
| 0.0004        | 12.0  | 3324 | 0.8328          | 0.7758 | 0.8306  | 0.4856   |


### Framework versions

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