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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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model-index: |
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- name: newsdiscourse-model-large |
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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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# newsdiscourse-model-large |
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5899 |
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- F1: 0.1975 |
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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: 1 |
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- eval_batch_size: 1 |
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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: linear |
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- num_epochs: 5.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| No log | 0.14 | 100 | 1.9895 | 0.0487 | |
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| No log | 0.28 | 200 | 2.0130 | 0.0512 | |
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| No log | 0.43 | 300 | 1.9527 | 0.0512 | |
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| No log | 0.57 | 400 | 1.9605 | 0.0487 | |
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| 2.0539 | 0.71 | 500 | 1.9854 | 0.0618 | |
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| 2.0539 | 0.85 | 600 | 1.7978 | 0.1242 | |
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| 2.0539 | 1.0 | 700 | 1.7291 | 0.1373 | |
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| 2.0539 | 1.14 | 800 | 1.9082 | 0.0487 | |
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| 2.0539 | 1.28 | 900 | 1.9300 | 0.0487 | |
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| 1.9096 | 1.42 | 1000 | 1.7186 | 0.1414 | |
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| 1.9096 | 1.57 | 1100 | 1.7304 | 0.1399 | |
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| 1.9096 | 1.71 | 1200 | 1.7281 | 0.1363 | |
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| 1.9096 | 1.85 | 1300 | 1.8452 | 0.0576 | |
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| 1.9096 | 1.99 | 1400 | 1.7180 | 0.1519 | |
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| 1.7842 | 2.14 | 1500 | 1.7450 | 0.1525 | |
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| 1.7842 | 2.28 | 1600 | 1.7752 | 0.1344 | |
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| 1.7842 | 2.42 | 1700 | 1.7548 | 0.1506 | |
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| 1.7842 | 2.56 | 1800 | 1.7185 | 0.1536 | |
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| 1.7842 | 2.71 | 1900 | 1.6870 | 0.1536 | |
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| 1.7227 | 2.85 | 2000 | 1.7336 | 0.1536 | |
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| 1.7227 | 2.99 | 2100 | 1.7217 | 0.1490 | |
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| 1.7227 | 3.13 | 2200 | 1.7213 | 0.1482 | |
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| 1.7227 | 3.28 | 2300 | 1.7482 | 0.1435 | |
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| 1.7227 | 3.42 | 2400 | 1.7559 | 0.1456 | |
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| 1.7441 | 3.56 | 2500 | 1.7324 | 0.1406 | |
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| 1.7441 | 3.7 | 2600 | 1.6977 | 0.1484 | |
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| 1.7441 | 3.85 | 2700 | 1.6276 | 0.1839 | |
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| 1.7441 | 3.99 | 2800 | 1.6109 | 0.1876 | |
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| 1.7441 | 4.13 | 2900 | 1.6359 | 0.2181 | |
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| 1.6515 | 4.27 | 3000 | 1.6463 | 0.1792 | |
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| 1.6515 | 4.42 | 3100 | 1.6397 | 0.1828 | |
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| 1.6515 | 4.56 | 3200 | 1.6189 | 0.1837 | |
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| 1.6515 | 4.7 | 3300 | 1.6096 | 0.1875 | |
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| 1.6515 | 4.84 | 3400 | 1.5904 | 0.1925 | |
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| 1.6003 | 4.99 | 3500 | 1.5899 | 0.1975 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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