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  1. README.md +79 -0
  2. generation_config.json +13 -0
  3. pytorch_model.bin +1 -1
README.md ADDED
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+ ---
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+ license: mit
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+ base_model: facebook/bart-large-xsum
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - rouge
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+ model-index:
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+ - name: text_shortening_model_v35
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+ results: []
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+ ---
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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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+
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+ # text_shortening_model_v35
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+
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+ This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.1783
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+ - Rouge1: 0.4993
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+ - Rouge2: 0.2724
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+ - Rougel: 0.4472
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+ - Rougelsum: 0.4467
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+ - Bert precision: 0.8744
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+ - Bert recall: 0.8769
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+ - Average word count: 8.6096
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+ - Max word count: 20
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+ - Min word count: 5
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+ - Average token count: 14.97
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+ - % shortened texts with length > 12: 3.6036
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0003
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
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+ | 1.83 | 1.0 | 37 | 1.9642 | 0.457 | 0.2329 | 0.4049 | 0.4054 | 0.8677 | 0.8663 | 8.027 | 13 | 4 | 16.6607 | 2.7027 |
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+ | 0.8629 | 2.0 | 74 | 1.6943 | 0.5268 | 0.3019 | 0.4695 | 0.4697 | 0.8758 | 0.8901 | 10.0571 | 19 | 5 | 17.8258 | 19.2192 |
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+ | 0.7849 | 3.0 | 111 | 1.6564 | 0.5001 | 0.279 | 0.4553 | 0.4554 | 0.873 | 0.8805 | 8.9099 | 17 | 5 | 15.4865 | 5.1051 |
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+ | 0.6116 | 4.0 | 148 | 1.7559 | 0.4638 | 0.2376 | 0.4183 | 0.4188 | 0.863 | 0.8665 | 8.4414 | 15 | 4 | 13.8829 | 0.9009 |
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+ | 0.3976 | 5.0 | 185 | 1.6708 | 0.4999 | 0.2723 | 0.4481 | 0.4481 | 0.8744 | 0.8766 | 8.5556 | 16 | 5 | 14.6877 | 3.9039 |
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+ | 0.2977 | 6.0 | 222 | 1.7196 | 0.4937 | 0.2699 | 0.4376 | 0.4379 | 0.8684 | 0.877 | 9.1652 | 20 | 5 | 15.3964 | 5.7057 |
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+ | 0.2187 | 7.0 | 259 | 1.7942 | 0.5129 | 0.2905 | 0.4572 | 0.4575 | 0.8765 | 0.8803 | 8.7117 | 19 | 5 | 14.6306 | 3.9039 |
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+ | 0.1603 | 8.0 | 296 | 1.8003 | 0.4822 | 0.2538 | 0.4237 | 0.4229 | 0.8688 | 0.8722 | 8.6306 | 19 | 5 | 15.4474 | 5.7057 |
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+ | 0.1175 | 9.0 | 333 | 2.0138 | 0.5024 | 0.2798 | 0.4486 | 0.4475 | 0.8742 | 0.8791 | 8.7988 | 19 | 5 | 16.1471 | 6.6066 |
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+ | 0.0859 | 10.0 | 370 | 2.1783 | 0.4993 | 0.2724 | 0.4472 | 0.4467 | 0.8744 | 0.8769 | 8.6096 | 20 | 5 | 14.97 | 3.6036 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.1
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.5
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+ - Tokenizers 0.13.3
generation_config.json ADDED
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+ {
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+ "bos_token_id": 0,
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+ "decoder_start_token_id": 2,
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+ "early_stopping": true,
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+ "eos_token_id": 2,
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+ "forced_eos_token_id": 2,
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+ "max_length": 62,
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+ "min_length": 11,
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+ "no_repeat_ngram_size": 3,
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+ "num_beams": 6,
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+ "pad_token_id": 1,
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+ "transformers_version": "4.33.1"
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+ }
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