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library_name: transformers
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: judge_answer___33_deberta_large_enwiki-answerability-2411
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. -->
# judge_answer___33_deberta_large_enwiki-answerability-2411
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2559
- Accuracy: 0.9392
- Precision: 0.9429
- Recall: 0.9326
- F1: 0.9377
- F0.5: 0.9409
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | F0.5 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.2133 | 0.0797 | 2000 | 0.2326 | 0.9218 | 0.9375 | 0.9007 | 0.9187 | 0.9299 |
| 0.2011 | 0.1593 | 4000 | 0.2527 | 0.9231 | 0.9084 | 0.9378 | 0.9229 | 0.9141 |
| 0.2094 | 0.2390 | 6000 | 0.2083 | 0.9256 | 0.9130 | 0.9378 | 0.9253 | 0.9179 |
| 0.1941 | 0.3186 | 8000 | 0.2156 | 0.9282 | 0.9460 | 0.9054 | 0.9253 | 0.9376 |
| 0.1933 | 0.3983 | 10000 | 0.2356 | 0.9290 | 0.9495 | 0.9033 | 0.9258 | 0.9399 |
| 0.1874 | 0.4779 | 12000 | 0.2501 | 0.9267 | 0.9325 | 0.9169 | 0.9247 | 0.9294 |
| 0.1849 | 0.5576 | 14000 | 0.2294 | 0.9272 | 0.9120 | 0.9425 | 0.9270 | 0.9180 |
| 0.1886 | 0.6372 | 16000 | 0.2367 | 0.9277 | 0.9554 | 0.8945 | 0.9239 | 0.9425 |
| 0.1865 | 0.7169 | 18000 | 0.1955 | 0.9356 | 0.9360 | 0.9326 | 0.9343 | 0.9353 |
| 0.1677 | 0.7966 | 20000 | 0.2023 | 0.9362 | 0.9398 | 0.9295 | 0.9346 | 0.9377 |
| 0.1662 | 0.8762 | 22000 | 0.2184 | 0.9341 | 0.9295 | 0.9368 | 0.9331 | 0.9309 |
| 0.163 | 0.9559 | 24000 | 0.2025 | 0.9408 | 0.9422 | 0.9368 | 0.9395 | 0.9411 |
| 0.1384 | 1.0355 | 26000 | 0.2516 | 0.9395 | 0.9463 | 0.9295 | 0.9378 | 0.9429 |
| 0.139 | 1.1152 | 28000 | 0.2647 | 0.9390 | 0.9397 | 0.9357 | 0.9377 | 0.9389 |
| 0.136 | 1.1948 | 30000 | 0.2608 | 0.9392 | 0.9458 | 0.9295 | 0.9375 | 0.9425 |
| 0.1431 | 1.2745 | 32000 | 0.2793 | 0.9351 | 0.9496 | 0.9164 | 0.9327 | 0.9428 |
| 0.1393 | 1.3542 | 34000 | 0.2370 | 0.9397 | 0.9454 | 0.9310 | 0.9381 | 0.9425 |
| 0.1325 | 1.4338 | 36000 | 0.2606 | 0.9369 | 0.9413 | 0.9295 | 0.9353 | 0.9389 |
| 0.1465 | 1.5135 | 38000 | 0.2371 | 0.9369 | 0.9450 | 0.9253 | 0.9351 | 0.9410 |
| 0.1254 | 1.5931 | 40000 | 0.2831 | 0.9367 | 0.9398 | 0.9305 | 0.9352 | 0.9380 |
| 0.1383 | 1.6728 | 42000 | 0.2655 | 0.9397 | 0.9458 | 0.9305 | 0.9381 | 0.9427 |
| 0.1386 | 1.7524 | 44000 | 0.2582 | 0.9385 | 0.9476 | 0.9258 | 0.9366 | 0.9432 |
| 0.1405 | 1.8321 | 46000 | 0.2535 | 0.9382 | 0.9400 | 0.9336 | 0.9368 | 0.9387 |
| 0.1428 | 1.9117 | 48000 | 0.2554 | 0.9392 | 0.9467 | 0.9284 | 0.9375 | 0.9430 |
| 0.1321 | 1.9914 | 50000 | 0.2559 | 0.9392 | 0.9429 | 0.9326 | 0.9377 | 0.9409 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.4.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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