---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: checkpoints_28_9_microsoft_deberta_V5
  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_V5

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: 0.6408
- Map@3: 0.8542
- Accuracy: 0.76

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 1.6111        | 0.05  | 25   | 1.6092          | 0.5092 | 0.325    |
| 1.6139        | 0.11  | 50   | 1.6085          | 0.7    | 0.575    |
| 1.6096        | 0.16  | 75   | 1.5867          | 0.7583 | 0.645    |
| 1.2905        | 0.21  | 100  | 1.1496          | 0.7767 | 0.66     |
| 1.0263        | 0.27  | 125  | 0.8628          | 0.8067 | 0.705    |
| 0.9475        | 0.32  | 150  | 0.7252          | 0.8458 | 0.75     |
| 0.841         | 0.37  | 175  | 0.7018          | 0.8492 | 0.76     |
| 0.8301        | 0.43  | 200  | 0.7137          | 0.8492 | 0.755    |
| 0.823         | 0.48  | 225  | 0.6633          | 0.8525 | 0.755    |
| 0.8263        | 0.53  | 250  | 0.6751          | 0.8608 | 0.765    |
| 0.7962        | 0.59  | 275  | 0.6704          | 0.8542 | 0.755    |
| 0.8013        | 0.64  | 300  | 0.6583          | 0.8525 | 0.755    |
| 0.789         | 0.69  | 325  | 0.6497          | 0.8533 | 0.76     |
| 0.7979        | 0.75  | 350  | 0.6512          | 0.8525 | 0.755    |
| 0.7751        | 0.8   | 375  | 0.6445          | 0.8583 | 0.765    |
| 0.7993        | 0.85  | 400  | 0.6424          | 0.8558 | 0.765    |
| 0.7685        | 0.91  | 425  | 0.6408          | 0.8542 | 0.76     |
| 0.7807        | 0.96  | 450  | 0.6408          | 0.8542 | 0.76     |


### Framework versions

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