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---
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
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