---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 1_microsoft_deberta_V1.1
  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. -->

# 1_microsoft_deberta_V1.1

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: 0.7138
- Map@3: 0.8492
- Accuracy: 0.775

## 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: 16
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 1.6141        | 0.03  | 50   | 1.6087          | 0.6242 | 0.51     |
| 1.336         | 0.05  | 100  | 1.1398          | 0.7550 | 0.645    |
| 0.9441        | 0.08  | 150  | 0.8809          | 0.8150 | 0.7      |
| 0.9279        | 0.11  | 200  | 0.7528          | 0.8383 | 0.73     |
| 0.8639        | 0.13  | 250  | 0.7259          | 0.8525 | 0.76     |
| 0.8255        | 0.16  | 300  | 0.7363          | 0.8592 | 0.785    |
| 0.8411        | 0.19  | 350  | 0.7052          | 0.8483 | 0.76     |
| 0.856         | 0.21  | 400  | 0.7097          | 0.8408 | 0.745    |
| 0.7753        | 0.24  | 450  | 0.6860          | 0.8575 | 0.775    |
| 0.7941        | 0.27  | 500  | 0.7146          | 0.8525 | 0.765    |
| 0.8062        | 0.29  | 550  | 0.7138          | 0.8492 | 0.775    |


### Framework versions

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