---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: checkpoints_28_9_microsoft_deberta_V2
  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_V2

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: 0.5675
- Map@3: 0.8842
- Accuracy: 0.815

## 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: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Map@3  | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 1.0011        | 0.11  | 100  | 0.8842          | 0.8258 | 0.74     |
| 0.8398        | 0.21  | 200  | 0.6978          | 0.8667 | 0.79     |
| 0.8414        | 0.32  | 300  | 0.6337          | 0.8625 | 0.795    |
| 0.7461        | 0.43  | 400  | 0.6609          | 0.8600 | 0.775    |
| 0.7131        | 0.53  | 500  | 0.6329          | 0.8758 | 0.805    |
| 0.6891        | 0.64  | 600  | 0.6157          | 0.8892 | 0.83     |
| 0.6969        | 0.75  | 700  | 0.5917          | 0.8808 | 0.805    |
| 0.6775        | 0.85  | 800  | 0.5698          | 0.8817 | 0.81     |
| 0.6534        | 0.96  | 900  | 0.5675          | 0.8842 | 0.815    |


### Framework versions

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