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---
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
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