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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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- allenai/mslr2022 |
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model-index: |
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- name: baseline |
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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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# Overview |
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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 allenai/mslr2022 ms2 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.7602 |
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- Rouge1 Fmeasure Mean: 28.5338 |
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- Rouge2 Fmeasure Mean: 9.5060 |
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- Rougel Fmeasure Mean: 20.9321 |
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- Rougelsum Fmeasure Mean: 24.0998 |
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- Bertscore Hashcode: microsoft/deberta-xlarge-mnli_L40_no-idf_version=0.3.11(hug_trans=4.21.0.dev0)-rescaled_fast-tokenizer |
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- Bertscore F1 Mean: 22.7619 |
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- Seed: 42 |
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- Model Name Or Path: allenai/led-base-16384 |
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- Doc Sep Token: </s> |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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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: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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- label_smoothing_factor: 0.1 |
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### Framework versions |
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- Transformers 4.21.0.dev0 |
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- Pytorch 1.10.0 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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