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--- |
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license: mit |
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base_model: microsoft/deberta-v3-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: checkpoints_29_9_microsoft_deberta_V1 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# checkpoints_29_9_microsoft_deberta_V1 |
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7815 |
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- Map@3: 0.8290 |
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- Accuracy: 0.7333 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-06 |
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- train_batch_size: 1 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Map@3 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| |
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| 1.6045 | 0.05 | 200 | 1.6095 | 0.4593 | 0.3030 | |
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| 1.3669 | 0.11 | 400 | 1.3360 | 0.7215 | 0.5980 | |
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| 0.9993 | 0.16 | 600 | 1.0403 | 0.7737 | 0.6727 | |
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| 0.9608 | 0.21 | 800 | 0.9539 | 0.7966 | 0.6990 | |
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| 0.9017 | 0.27 | 1000 | 0.9125 | 0.7997 | 0.6970 | |
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| 0.885 | 0.32 | 1200 | 0.8719 | 0.8172 | 0.7192 | |
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| 0.8222 | 0.37 | 1400 | 0.8462 | 0.8125 | 0.7030 | |
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| 0.769 | 0.43 | 1600 | 0.8376 | 0.8158 | 0.7131 | |
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| 0.7676 | 0.48 | 1800 | 0.8109 | 0.8178 | 0.7152 | |
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| 0.8413 | 0.53 | 2000 | 0.8279 | 0.8212 | 0.7212 | |
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| 0.809 | 0.59 | 2200 | 0.8012 | 0.8212 | 0.7212 | |
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| 0.8809 | 0.64 | 2400 | 0.8037 | 0.8290 | 0.7333 | |
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| 0.8028 | 0.69 | 2600 | 0.7949 | 0.8249 | 0.7293 | |
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| 0.8259 | 0.75 | 2800 | 0.7938 | 0.8283 | 0.7354 | |
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| 0.7548 | 0.8 | 3000 | 0.7818 | 0.8300 | 0.7354 | |
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| 0.7422 | 0.85 | 3200 | 0.7797 | 0.8316 | 0.7374 | |
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| 0.801 | 0.91 | 3400 | 0.7811 | 0.8303 | 0.7354 | |
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| 0.7 | 0.96 | 3600 | 0.7815 | 0.8290 | 0.7333 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.0 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.3 |
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