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text_shortening_model_v5

Text2Text Generation
Transformers
PyTorch
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Community
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  1. README.md +118 -0
  2. generation_config.json +6 -0
  3. pytorch_model.bin +1 -1
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: t5-small
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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_v5
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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_v5
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+
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+ This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.3950
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+ - Rouge1: 0.6032
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+ - Rouge2: 0.3745
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+ - Rougel: 0.5559
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+ - Rougelsum: 0.556
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+ - Bert precision: 0.8961
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+ - Bert recall: 0.9059
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+ - Average word count: 11.4071
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+ - Max word count: 16
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+ - Min word count: 6
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+ - Average token count: 16.7643
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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: 0.0001
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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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: 50
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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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|
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+ | 1.311 | 1.0 | 8 | 1.8181 | 0.5439 | 0.3249 | 0.4963 | 0.4961 | 0.879 | 0.8847 | 11.65 | 18 | 1 | 16.8857 |
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+ | 1.174 | 2.0 | 16 | 1.6800 | 0.55 | 0.3147 | 0.4935 | 0.4931 | 0.8779 | 0.8891 | 12.1214 | 18 | 5 | 17.2857 |
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+ | 1.1265 | 3.0 | 24 | 1.6149 | 0.5642 | 0.3349 | 0.5109 | 0.5105 | 0.8833 | 0.8935 | 11.8643 | 18 | 5 | 16.9571 |
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+ | 1.1075 | 4.0 | 32 | 1.5730 | 0.5657 | 0.3383 | 0.5163 | 0.5161 | 0.8836 | 0.8961 | 11.9643 | 18 | 4 | 17.0929 |
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+ | 1.062 | 5.0 | 40 | 1.5421 | 0.5819 | 0.3544 | 0.53 | 0.5292 | 0.8858 | 0.9007 | 12.1286 | 18 | 5 | 17.2571 |
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+ | 1.021 | 6.0 | 48 | 1.5085 | 0.5792 | 0.3514 | 0.5262 | 0.5255 | 0.8848 | 0.8986 | 11.9929 | 18 | 5 | 17.1 |
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+ | 0.998 | 7.0 | 56 | 1.4826 | 0.5825 | 0.3548 | 0.5335 | 0.5317 | 0.887 | 0.9 | 11.8357 | 18 | 6 | 17.0857 |
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+ | 0.9794 | 8.0 | 64 | 1.4659 | 0.5814 | 0.3508 | 0.5306 | 0.5297 | 0.8877 | 0.8993 | 11.6714 | 18 | 4 | 16.9286 |
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+ | 0.9553 | 9.0 | 72 | 1.4533 | 0.5871 | 0.3545 | 0.533 | 0.5318 | 0.8874 | 0.9018 | 11.8857 | 18 | 6 | 17.2071 |
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+ | 0.9451 | 10.0 | 80 | 1.4402 | 0.5871 | 0.3604 | 0.5368 | 0.5361 | 0.8889 | 0.9013 | 11.6571 | 18 | 6 | 16.9929 |
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+ | 0.9223 | 11.0 | 88 | 1.4334 | 0.5888 | 0.3602 | 0.5378 | 0.5369 | 0.8883 | 0.9017 | 11.8071 | 18 | 6 | 17.1643 |
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+ | 0.893 | 12.0 | 96 | 1.4295 | 0.587 | 0.3589 | 0.5367 | 0.5356 | 0.8878 | 0.9008 | 11.8 | 18 | 6 | 17.1214 |
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+ | 0.8768 | 13.0 | 104 | 1.4182 | 0.5887 | 0.3598 | 0.5395 | 0.5388 | 0.8887 | 0.9021 | 11.8571 | 17 | 6 | 17.2429 |
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+ | 0.8598 | 14.0 | 112 | 1.4076 | 0.5937 | 0.3647 | 0.5476 | 0.5466 | 0.8909 | 0.9021 | 11.6214 | 16 | 6 | 16.9429 |
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+ | 0.8555 | 15.0 | 120 | 1.4080 | 0.5948 | 0.3668 | 0.5481 | 0.5473 | 0.89 | 0.9018 | 11.6786 | 16 | 6 | 17.0429 |
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+ | 0.8505 | 16.0 | 128 | 1.4067 | 0.5984 | 0.3705 | 0.5517 | 0.5507 | 0.8908 | 0.9031 | 11.7214 | 17 | 6 | 17.0714 |
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+ | 0.8545 | 17.0 | 136 | 1.3995 | 0.5946 | 0.3669 | 0.5479 | 0.547 | 0.8924 | 0.9028 | 11.55 | 15 | 6 | 16.9071 |
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+ | 0.8025 | 18.0 | 144 | 1.3953 | 0.5935 | 0.3637 | 0.547 | 0.5461 | 0.8924 | 0.9022 | 11.5571 | 15 | 6 | 16.8929 |
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+ | 0.7915 | 19.0 | 152 | 1.3975 | 0.5963 | 0.3702 | 0.5485 | 0.5476 | 0.8899 | 0.9025 | 11.7714 | 17 | 6 | 17.1929 |
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+ | 0.8017 | 20.0 | 160 | 1.3957 | 0.5915 | 0.3633 | 0.5439 | 0.542 | 0.8897 | 0.902 | 11.7143 | 17 | 6 | 17.1643 |
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+ | 0.8133 | 21.0 | 168 | 1.3926 | 0.5932 | 0.3632 | 0.5438 | 0.5425 | 0.8916 | 0.9022 | 11.5714 | 16 | 6 | 16.9786 |
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+ | 0.7858 | 22.0 | 176 | 1.3942 | 0.5941 | 0.3658 | 0.5453 | 0.544 | 0.8915 | 0.9022 | 11.5714 | 16 | 6 | 16.9857 |
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+ | 0.7712 | 23.0 | 184 | 1.3929 | 0.6015 | 0.3698 | 0.5506 | 0.5498 | 0.8916 | 0.9044 | 11.7714 | 16 | 6 | 17.1786 |
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+ | 0.7786 | 24.0 | 192 | 1.3900 | 0.5985 | 0.3662 | 0.549 | 0.5482 | 0.8926 | 0.903 | 11.5286 | 16 | 6 | 16.8857 |
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+ | 0.7707 | 25.0 | 200 | 1.3888 | 0.6011 | 0.3708 | 0.5508 | 0.5495 | 0.8947 | 0.9037 | 11.3786 | 15 | 6 | 16.7286 |
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+ | 0.7661 | 26.0 | 208 | 1.3888 | 0.6001 | 0.3704 | 0.5512 | 0.55 | 0.8943 | 0.9033 | 11.4429 | 15 | 6 | 16.8 |
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+ | 0.7489 | 27.0 | 216 | 1.3892 | 0.5953 | 0.3673 | 0.5467 | 0.5462 | 0.8927 | 0.9017 | 11.4429 | 15 | 6 | 16.7929 |
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+ | 0.7433 | 28.0 | 224 | 1.3910 | 0.5925 | 0.3661 | 0.5449 | 0.5449 | 0.8927 | 0.9023 | 11.4714 | 15 | 6 | 16.9 |
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+ | 0.7295 | 29.0 | 232 | 1.3886 | 0.5934 | 0.3656 | 0.5458 | 0.5451 | 0.893 | 0.9019 | 11.4929 | 15 | 6 | 16.8429 |
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+ | 0.7446 | 30.0 | 240 | 1.3874 | 0.5947 | 0.3643 | 0.5474 | 0.5471 | 0.893 | 0.9017 | 11.4929 | 15 | 6 | 16.7786 |
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+ | 0.7318 | 31.0 | 248 | 1.3848 | 0.5998 | 0.3708 | 0.5518 | 0.5517 | 0.8946 | 0.9029 | 11.5 | 15 | 6 | 16.7714 |
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+ | 0.7279 | 32.0 | 256 | 1.3851 | 0.6003 | 0.3703 | 0.5522 | 0.5522 | 0.8948 | 0.9035 | 11.5214 | 15 | 6 | 16.7929 |
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+ | 0.725 | 33.0 | 264 | 1.3879 | 0.5979 | 0.3677 | 0.5487 | 0.5476 | 0.8956 | 0.9046 | 11.4643 | 15 | 6 | 16.7214 |
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+ | 0.7229 | 34.0 | 272 | 1.3907 | 0.5959 | 0.3677 | 0.5463 | 0.5457 | 0.8948 | 0.904 | 11.5286 | 15 | 6 | 16.8143 |
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+ | 0.7228 | 35.0 | 280 | 1.3916 | 0.5983 | 0.3696 | 0.5499 | 0.5491 | 0.8947 | 0.9047 | 11.5857 | 15 | 6 | 16.8714 |
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+ | 0.7006 | 36.0 | 288 | 1.3913 | 0.5962 | 0.3681 | 0.5461 | 0.5454 | 0.8938 | 0.9036 | 11.5571 | 15 | 6 | 16.8286 |
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+ | 0.6935 | 37.0 | 296 | 1.3891 | 0.5976 | 0.3707 | 0.55 | 0.5496 | 0.895 | 0.9042 | 11.3786 | 15 | 6 | 16.6857 |
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+ | 0.7011 | 38.0 | 304 | 1.3894 | 0.602 | 0.3727 | 0.5546 | 0.554 | 0.8965 | 0.9059 | 11.4429 | 16 | 6 | 16.6929 |
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+ | 0.7188 | 39.0 | 312 | 1.3903 | 0.6031 | 0.373 | 0.5556 | 0.5548 | 0.896 | 0.9061 | 11.5357 | 16 | 6 | 16.7929 |
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+ | 0.7013 | 40.0 | 320 | 1.3927 | 0.6055 | 0.3763 | 0.5573 | 0.5564 | 0.8952 | 0.906 | 11.5929 | 16 | 6 | 16.8929 |
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+ | 0.6857 | 41.0 | 328 | 1.3932 | 0.5991 | 0.3729 | 0.5509 | 0.5514 | 0.894 | 0.9054 | 11.5357 | 16 | 6 | 16.8857 |
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+ | 0.7063 | 42.0 | 336 | 1.3933 | 0.5995 | 0.3739 | 0.5514 | 0.5513 | 0.8943 | 0.9056 | 11.5571 | 16 | 6 | 16.8571 |
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+ | 0.7022 | 43.0 | 344 | 1.3935 | 0.5974 | 0.3714 | 0.55 | 0.5503 | 0.894 | 0.9052 | 11.55 | 16 | 6 | 16.8714 |
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+ | 0.6975 | 44.0 | 352 | 1.3937 | 0.6008 | 0.369 | 0.5519 | 0.5516 | 0.8949 | 0.905 | 11.5286 | 16 | 6 | 16.8071 |
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+ | 0.687 | 45.0 | 360 | 1.3937 | 0.6024 | 0.3705 | 0.5536 | 0.5534 | 0.8955 | 0.9053 | 11.4929 | 16 | 6 | 16.7786 |
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+ | 0.7044 | 46.0 | 368 | 1.3944 | 0.6024 | 0.3718 | 0.5545 | 0.5543 | 0.8957 | 0.9054 | 11.4643 | 16 | 6 | 16.7714 |
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+ | 0.695 | 47.0 | 376 | 1.3947 | 0.6037 | 0.3746 | 0.5558 | 0.5556 | 0.896 | 0.9059 | 11.45 | 16 | 6 | 16.7857 |
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+ | 0.7019 | 48.0 | 384 | 1.3949 | 0.6047 | 0.3756 | 0.5575 | 0.5572 | 0.896 | 0.9058 | 11.4357 | 16 | 6 | 16.7643 |
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+ | 0.6895 | 49.0 | 392 | 1.3950 | 0.6032 | 0.3745 | 0.5559 | 0.556 | 0.8961 | 0.9059 | 11.4071 | 16 | 6 | 16.7643 |
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+ | 0.6914 | 50.0 | 400 | 1.3950 | 0.6032 | 0.3745 | 0.5559 | 0.556 | 0.8961 | 0.9059 | 11.4071 | 16 | 6 | 16.7643 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.32.1
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.4
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+ - Tokenizers 0.13.3
generation_config.json ADDED
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+ "pad_token_id": 0,
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+ "transformers_version": "4.32.1"
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+ }
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