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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- BEE-spoke-data/goodwiki-deduped-split |
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metrics: |
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- accuracy |
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model-index: |
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- name: bitllama-goodwiki |
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results: |
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- task: |
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name: Causal Language Modeling |
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type: text-generation |
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dataset: |
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name: BEE-spoke-data/goodwiki-deduped-split |
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type: BEE-spoke-data/goodwiki-deduped-split |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.4285134482793542 |
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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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# bitllama-goodwiki |
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This model was trained from scratch on the BEE-spoke-data/goodwiki-deduped-split dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.0525 |
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- Accuracy: 0.4285 |
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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: 0.0008 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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_steps: 100 |
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- num_epochs: 1.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 6.1199 | 0.04 | 100 | 6.0749 | 0.1542 | |
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| 5.3869 | 0.07 | 200 | 5.3267 | 0.2032 | |
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| 4.9187 | 0.11 | 300 | 4.8566 | 0.2386 | |
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| 4.6185 | 0.14 | 400 | 4.5535 | 0.2624 | |
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| 4.3509 | 0.18 | 500 | 4.3388 | 0.2801 | |
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| 4.1666 | 0.21 | 600 | 4.1692 | 0.2956 | |
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| 4.0456 | 0.25 | 700 | 4.0399 | 0.3089 | |
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| 3.9273 | 0.28 | 800 | 3.9318 | 0.3193 | |
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| 3.8447 | 0.32 | 900 | 3.8173 | 0.3327 | |
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| 3.7143 | 0.35 | 1000 | 3.7108 | 0.3461 | |
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| 3.6485 | 0.39 | 1100 | 3.6116 | 0.3590 | |
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| 3.5171 | 0.42 | 1200 | 3.5303 | 0.3693 | |
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| 3.4464 | 0.46 | 1300 | 3.4554 | 0.3780 | |
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| 3.3955 | 0.49 | 1400 | 3.3999 | 0.3851 | |
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| 3.3551 | 0.53 | 1500 | 3.3432 | 0.3919 | |
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| 3.2787 | 0.56 | 1600 | 3.2981 | 0.3974 | |
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| 3.2705 | 0.6 | 1700 | 3.2566 | 0.4023 | |
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| 3.2281 | 0.64 | 1800 | 3.2172 | 0.4075 | |
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| 3.1759 | 0.67 | 1900 | 3.1826 | 0.4118 | |
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| 3.1603 | 0.71 | 2000 | 3.1547 | 0.4152 | |
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| 3.1328 | 0.74 | 2100 | 3.1283 | 0.4186 | |
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| 3.0916 | 0.78 | 2200 | 3.1055 | 0.4215 | |
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| 3.0939 | 0.81 | 2300 | 3.0875 | 0.4238 | |
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| 3.0584 | 0.85 | 2400 | 3.0732 | 0.4257 | |
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| 3.0711 | 0.88 | 2500 | 3.0631 | 0.4271 | |
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| 3.0612 | 0.92 | 2600 | 3.0565 | 0.4280 | |
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| 3.081 | 0.95 | 2700 | 3.0534 | 0.4284 | |
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| 3.0378 | 0.99 | 2800 | 3.0525 | 0.4285 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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