---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
model-index:
- name: checkpoints_2
  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_2

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: 0.8543
- Map@3: 0.7167

## 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: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Map@3  |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.395         | 0.19  | 25   | 1.3859          | 0.5889 |
| 1.3803        | 0.37  | 50   | 1.3840          | 0.6958 |
| 1.3842        | 0.56  | 75   | 1.3314          | 0.7194 |
| 1.2795        | 0.74  | 100  | 1.0021          | 0.7222 |
| 0.9662        | 0.93  | 125  | 0.9006          | 0.6597 |
| 0.9574        | 1.11  | 150  | 0.8355          | 0.6903 |
| 0.8909        | 1.3   | 175  | 0.8506          | 0.6750 |
| 0.8077        | 1.48  | 200  | 0.8180          | 0.7125 |
| 0.955         | 1.67  | 225  | 0.8069          | 0.7097 |
| 0.8664        | 1.85  | 250  | 0.8186          | 0.7028 |
| 0.9396        | 2.04  | 275  | 0.8091          | 0.6986 |
| 0.8141        | 2.22  | 300  | 0.8212          | 0.7083 |
| 0.7898        | 2.41  | 325  | 0.8531          | 0.7167 |
| 0.9143        | 2.59  | 350  | 0.8482          | 0.7125 |
| 0.8861        | 2.78  | 375  | 0.8229          | 0.7083 |
| 0.8569        | 2.96  | 400  | 0.8372          | 0.7181 |
| 0.8381        | 3.15  | 425  | 0.8516          | 0.7153 |
| 0.7671        | 3.33  | 450  | 0.8458          | 0.7167 |
| 0.8704        | 3.52  | 475  | 0.8651          | 0.7222 |
| 0.8733        | 3.7   | 500  | 0.8356          | 0.7153 |
| 0.7309        | 3.89  | 525  | 0.8476          | 0.7181 |
| 0.7793        | 4.07  | 550  | 0.8566          | 0.7167 |
| 0.7849        | 4.26  | 575  | 0.8644          | 0.7167 |
| 0.7776        | 4.44  | 600  | 0.8584          | 0.7167 |
| 0.7573        | 4.63  | 625  | 0.8546          | 0.7167 |
| 0.8115        | 4.81  | 650  | 0.8543          | 0.7167 |
| 0.869         | 5.0   | 675  | 0.8543          | 0.7167 |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1