---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: checkpoints_28_9_microsoft_deberta_V4
  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. -->

# checkpoints_28_9_microsoft_deberta_V4

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2854
- Map@3: 0.5483
- Accuracy: 0.435

## 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: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Map@3  | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 1.2365        | 0.11  | 100  | 1.0631          | 0.7583 | 0.64     |
| 0.8608        | 0.21  | 200  | 0.7329          | 0.8383 | 0.75     |
| 0.8527        | 0.32  | 300  | 0.6985          | 0.8575 | 0.78     |
| 0.744         | 0.43  | 400  | 0.6498          | 0.8625 | 0.785    |
| 0.7686        | 0.53  | 500  | 0.7450          | 0.8575 | 0.765    |
| 1.4098        | 0.64  | 600  | 1.3030          | 0.5575 | 0.4      |
| 1.4246        | 0.75  | 700  | 1.3018          | 0.5575 | 0.435    |
| 1.3987        | 0.85  | 800  | 1.2906          | 0.5450 | 0.41     |
| 1.4121        | 0.96  | 900  | 1.2854          | 0.5483 | 0.435    |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.0
- Datasets 2.9.0
- Tokenizers 0.13.3