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---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large-offensive-lr7e-6-gas2-ls0.0
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. -->
# deberta-v3-large-offensive-lr7e-6-gas2-ls0.0
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: 1.3326
- Accuracy: 0.7832
## 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: 7e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6417 | 0.27 | 100 | 0.6283 | 0.6533 |
| 0.5105 | 0.54 | 200 | 0.4588 | 0.7915 |
| 0.4554 | 0.81 | 300 | 0.4500 | 0.7968 |
| 0.4212 | 1.08 | 400 | 0.4773 | 0.7938 |
| 0.4054 | 1.34 | 500 | 0.4311 | 0.7983 |
| 0.3922 | 1.61 | 600 | 0.4588 | 0.7998 |
| 0.3776 | 1.88 | 700 | 0.4367 | 0.8066 |
| 0.3535 | 2.15 | 800 | 0.4675 | 0.8074 |
| 0.33 | 2.42 | 900 | 0.4874 | 0.8021 |
| 0.3113 | 2.69 | 1000 | 0.4949 | 0.8044 |
| 0.3203 | 2.96 | 1100 | 0.4550 | 0.8059 |
| 0.248 | 3.23 | 1200 | 0.4858 | 0.8036 |
| 0.2478 | 3.49 | 1300 | 0.5299 | 0.8029 |
| 0.2371 | 3.76 | 1400 | 0.5013 | 0.7991 |
| 0.2388 | 4.03 | 1500 | 0.5520 | 0.8021 |
| 0.1744 | 4.3 | 1600 | 0.6687 | 0.7915 |
| 0.1788 | 4.57 | 1700 | 0.7560 | 0.7689 |
| 0.1652 | 4.84 | 1800 | 0.6985 | 0.7832 |
| 0.1596 | 5.11 | 1900 | 0.7191 | 0.7915 |
| 0.1214 | 5.38 | 2000 | 0.9097 | 0.7893 |
| 0.1432 | 5.64 | 2100 | 0.9184 | 0.7787 |
| 0.1145 | 5.91 | 2200 | 0.9620 | 0.7878 |
| 0.1069 | 6.18 | 2300 | 0.9489 | 0.7893 |
| 0.1012 | 6.45 | 2400 | 1.0107 | 0.7817 |
| 0.0942 | 6.72 | 2500 | 1.0021 | 0.7885 |
| 0.087 | 6.99 | 2600 | 1.1090 | 0.7915 |
| 0.0598 | 7.26 | 2700 | 1.1735 | 0.7795 |
| 0.0742 | 7.53 | 2800 | 1.1433 | 0.7817 |
| 0.073 | 7.79 | 2900 | 1.1343 | 0.7953 |
| 0.0553 | 8.06 | 3000 | 1.2258 | 0.7840 |
| 0.0474 | 8.33 | 3100 | 1.2461 | 0.7817 |
| 0.0515 | 8.6 | 3200 | 1.2996 | 0.7825 |
| 0.0551 | 8.87 | 3300 | 1.2819 | 0.7855 |
| 0.0541 | 9.14 | 3400 | 1.2808 | 0.7855 |
| 0.0465 | 9.41 | 3500 | 1.3398 | 0.7817 |
| 0.0407 | 9.68 | 3600 | 1.3231 | 0.7825 |
| 0.0343 | 9.94 | 3700 | 1.3330 | 0.7825 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.9.0
- Datasets 2.2.2
- Tokenizers 0.11.6
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