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--- |
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license: apache-2.0 |
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base_model: allenai/led-base-16384 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: hf |
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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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# hf |
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This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2202 |
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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: 3e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: polynomial |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 20000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 0.255 | 0.0 | 500 | 0.2798 | |
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| 0.211 | 0.01 | 1000 | 0.2655 | |
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| 0.2399 | 0.01 | 1500 | 0.2625 | |
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| 0.2183 | 0.01 | 2000 | 0.2533 | |
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| 0.2375 | 0.02 | 2500 | 0.2501 | |
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| 0.2452 | 0.02 | 3000 | 0.2477 | |
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| 0.2636 | 0.02 | 3500 | 0.2474 | |
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| 0.2289 | 0.03 | 4000 | 0.2476 | |
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| 0.2453 | 0.03 | 4500 | 0.2443 | |
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| 0.2081 | 0.03 | 5000 | 0.2441 | |
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| 0.1655 | 0.04 | 5500 | 0.2422 | |
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| 0.2765 | 0.04 | 6000 | 0.2386 | |
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| 0.2797 | 0.05 | 6500 | 0.2398 | |
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| 0.1902 | 0.05 | 7000 | 0.2393 | |
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| 0.1875 | 0.05 | 7500 | 0.2363 | |
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| 0.2048 | 0.06 | 8000 | 0.2371 | |
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| 0.1695 | 0.06 | 8500 | 0.2341 | |
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| 0.2121 | 0.06 | 9000 | 0.2348 | |
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| 0.2402 | 0.07 | 9500 | 0.2331 | |
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| 0.1865 | 0.07 | 10000 | 0.2324 | |
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| 0.1862 | 0.07 | 10500 | 0.2329 | |
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| 0.2382 | 0.08 | 11000 | 0.2323 | |
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| 0.2703 | 0.08 | 11500 | 0.2300 | |
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| 0.2303 | 0.08 | 12000 | 0.2297 | |
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| 0.2258 | 0.09 | 12500 | 0.2287 | |
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| 0.1924 | 0.09 | 13000 | 0.2271 | |
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| 0.1849 | 0.09 | 13500 | 0.2269 | |
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| 0.2288 | 0.1 | 14000 | 0.2257 | |
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| 0.2461 | 0.1 | 14500 | 0.2259 | |
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| 0.2509 | 0.1 | 15000 | 0.2241 | |
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| 0.2087 | 0.11 | 15500 | 0.2245 | |
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| 0.1707 | 0.11 | 16000 | 0.2243 | |
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| 0.2053 | 0.11 | 16500 | 0.2238 | |
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| 0.2157 | 0.12 | 17000 | 0.2225 | |
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| 0.1976 | 0.12 | 17500 | 0.2218 | |
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| 0.2626 | 0.13 | 18000 | 0.2213 | |
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| 0.2032 | 0.13 | 18500 | 0.2209 | |
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| 0.2928 | 0.13 | 19000 | 0.2207 | |
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| 0.2438 | 0.14 | 19500 | 0.2205 | |
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| 0.2028 | 0.14 | 20000 | 0.2202 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.1 |
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