---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: checkpoints_2_microsoft_deberta_21_9
  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_microsoft_deberta_21_9

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8635
- Map@3: 0.8558
- 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Map@3  | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.6215        | 0.15  | 300  | 0.6511          | 0.8592 | 0.76     |
| 0.5953        | 0.3   | 600  | 0.6929          | 0.8533 | 0.765    |
| 0.5332        | 0.45  | 900  | 0.6665          | 0.8525 | 0.76     |
| 0.587         | 0.6   | 1200 | 0.6638          | 0.855  | 0.775    |
| 0.5626        | 0.75  | 1500 | 0.6476          | 0.8692 | 0.78     |
| 0.6712        | 0.9   | 1800 | 0.6499          | 0.8700 | 0.785    |
| 0.2181        | 1.05  | 2100 | 0.8619          | 0.8417 | 0.75     |
| 0.2024        | 1.2   | 2400 | 0.8607          | 0.8467 | 0.75     |
| 0.2571        | 1.35  | 2700 | 0.8282          | 0.8483 | 0.75     |
| 0.2407        | 1.5   | 3000 | 0.8297          | 0.8558 | 0.765    |
| 0.2282        | 1.65  | 3300 | 0.8635          | 0.8558 | 0.76     |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3