---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: checkpoints_29_9_microsoft_deberta_V1
  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_29_9_microsoft_deberta_V1

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: 0.7815
- Map@3: 0.8290
- Accuracy: 0.7333

## 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-06
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 1.6045        | 0.05  | 200  | 1.6095          | 0.4593 | 0.3030   |
| 1.3669        | 0.11  | 400  | 1.3360          | 0.7215 | 0.5980   |
| 0.9993        | 0.16  | 600  | 1.0403          | 0.7737 | 0.6727   |
| 0.9608        | 0.21  | 800  | 0.9539          | 0.7966 | 0.6990   |
| 0.9017        | 0.27  | 1000 | 0.9125          | 0.7997 | 0.6970   |
| 0.885         | 0.32  | 1200 | 0.8719          | 0.8172 | 0.7192   |
| 0.8222        | 0.37  | 1400 | 0.8462          | 0.8125 | 0.7030   |
| 0.769         | 0.43  | 1600 | 0.8376          | 0.8158 | 0.7131   |
| 0.7676        | 0.48  | 1800 | 0.8109          | 0.8178 | 0.7152   |
| 0.8413        | 0.53  | 2000 | 0.8279          | 0.8212 | 0.7212   |
| 0.809         | 0.59  | 2200 | 0.8012          | 0.8212 | 0.7212   |
| 0.8809        | 0.64  | 2400 | 0.8037          | 0.8290 | 0.7333   |
| 0.8028        | 0.69  | 2600 | 0.7949          | 0.8249 | 0.7293   |
| 0.8259        | 0.75  | 2800 | 0.7938          | 0.8283 | 0.7354   |
| 0.7548        | 0.8   | 3000 | 0.7818          | 0.8300 | 0.7354   |
| 0.7422        | 0.85  | 3200 | 0.7797          | 0.8316 | 0.7374   |
| 0.801         | 0.91  | 3400 | 0.7811          | 0.8303 | 0.7354   |
| 0.7           | 0.96  | 3600 | 0.7815          | 0.8290 | 0.7333   |


### Framework versions

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