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text_shortening_model_v58

Text2Text Generation
Transformers
PyTorch
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Community
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  1. README.md +120 -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_v58
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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_v58
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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: 0.7969
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+ - Rouge1: 0.6672
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+ - Rouge2: 0.4657
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+ - Rougel: 0.6067
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+ - Rougelsum: 0.6067
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+ - Bert precision: 0.9113
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+ - Bert recall: 0.9013
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+ - Bert f1-score: 0.9059
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+ - Average word count: 8.058
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+ - Max word count: 16
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+ - Min word count: 3
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+ - Average token count: 12.3438
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+ - % shortened texts with length > 12: 4.4643
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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: 1e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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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 | Bert f1-score | 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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+ | 1.4028 | 1.0 | 49 | 1.9552 | 0.313 | 0.162 | 0.275 | 0.2756 | 0.7453 | 0.7745 | 0.7581 | 9.7455 | 18 | 0 | 16.3661 | 25.4464 |
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+ | 1.1908 | 2.0 | 98 | 1.6121 | 0.2964 | 0.1484 | 0.2626 | 0.2629 | 0.7359 | 0.78 | 0.7563 | 9.9464 | 18 | 0 | 17.0268 | 21.875 |
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+ | 1.0226 | 3.0 | 147 | 1.3961 | 0.4308 | 0.2552 | 0.388 | 0.3874 | 0.8166 | 0.8275 | 0.8206 | 9.1339 | 18 | 0 | 14.9375 | 23.6607 |
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+ | 0.9468 | 4.0 | 196 | 1.2672 | 0.5074 | 0.3277 | 0.4695 | 0.4688 | 0.8525 | 0.8443 | 0.8474 | 8.3482 | 18 | 0 | 13.5938 | 15.625 |
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+ | 0.9222 | 5.0 | 245 | 1.1779 | 0.5567 | 0.3752 | 0.5141 | 0.5136 | 0.8764 | 0.8648 | 0.8698 | 8.192 | 18 | 0 | 12.9643 | 12.0536 |
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+ | 0.9229 | 6.0 | 294 | 1.1157 | 0.5911 | 0.4085 | 0.5432 | 0.5422 | 0.8855 | 0.8739 | 0.8791 | 8.0089 | 18 | 0 | 12.75 | 7.5893 |
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+ | 0.8773 | 7.0 | 343 | 1.0715 | 0.6099 | 0.4151 | 0.5546 | 0.5537 | 0.8969 | 0.8844 | 0.8901 | 7.9464 | 18 | 0 | 12.5312 | 8.4821 |
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+ | 0.8911 | 8.0 | 392 | 1.0372 | 0.62 | 0.4184 | 0.5617 | 0.5606 | 0.9016 | 0.8905 | 0.8956 | 8.1652 | 18 | 3 | 12.6429 | 8.9286 |
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+ | 0.8681 | 9.0 | 441 | 1.0105 | 0.6275 | 0.4279 | 0.571 | 0.5693 | 0.904 | 0.8932 | 0.8981 | 8.2723 | 18 | 3 | 12.5982 | 9.375 |
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+ | 0.8661 | 10.0 | 490 | 0.9883 | 0.6266 | 0.4226 | 0.5687 | 0.5675 | 0.9038 | 0.8931 | 0.898 | 8.317 | 18 | 3 | 12.6161 | 10.7143 |
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+ | 0.8606 | 11.0 | 539 | 0.9717 | 0.629 | 0.4283 | 0.5717 | 0.5702 | 0.9052 | 0.8934 | 0.8988 | 8.1875 | 17 | 3 | 12.4509 | 8.4821 |
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+ | 0.8701 | 12.0 | 588 | 0.9535 | 0.635 | 0.436 | 0.581 | 0.5799 | 0.9081 | 0.8941 | 0.9006 | 7.9062 | 15 | 3 | 12.183 | 6.25 |
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+ | 0.8449 | 13.0 | 637 | 0.9394 | 0.6381 | 0.4373 | 0.5846 | 0.5831 | 0.9088 | 0.8955 | 0.9016 | 7.9196 | 15 | 3 | 12.1696 | 5.3571 |
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+ | 0.8328 | 14.0 | 686 | 0.9270 | 0.6405 | 0.4455 | 0.5868 | 0.586 | 0.9083 | 0.8959 | 0.9016 | 7.9554 | 15 | 3 | 12.183 | 5.3571 |
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+ | 0.8448 | 15.0 | 735 | 0.9135 | 0.6449 | 0.4548 | 0.594 | 0.5926 | 0.909 | 0.8986 | 0.9033 | 8.0625 | 16 | 3 | 12.3616 | 5.8036 |
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+ | 0.8107 | 16.0 | 784 | 0.9028 | 0.6435 | 0.4484 | 0.5876 | 0.5868 | 0.9092 | 0.8979 | 0.9031 | 7.9911 | 15 | 3 | 12.25 | 4.9107 |
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+ | 0.831 | 17.0 | 833 | 0.8949 | 0.6458 | 0.4525 | 0.59 | 0.5887 | 0.9095 | 0.8989 | 0.9037 | 8.0491 | 15 | 3 | 12.308 | 5.3571 |
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+ | 0.8324 | 18.0 | 882 | 0.8849 | 0.6477 | 0.4495 | 0.5888 | 0.5874 | 0.9103 | 0.8989 | 0.9041 | 8.0491 | 15 | 3 | 12.3259 | 5.3571 |
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+ | 0.8404 | 19.0 | 931 | 0.8783 | 0.6522 | 0.4531 | 0.5915 | 0.5906 | 0.9109 | 0.8996 | 0.9048 | 8.0938 | 15 | 3 | 12.3795 | 5.8036 |
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+ | 0.8152 | 20.0 | 980 | 0.8694 | 0.6523 | 0.4545 | 0.5926 | 0.5921 | 0.9119 | 0.8996 | 0.9053 | 7.9821 | 15 | 3 | 12.2321 | 4.9107 |
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+ | 0.802 | 21.0 | 1029 | 0.8654 | 0.6559 | 0.4572 | 0.5954 | 0.5951 | 0.9117 | 0.9002 | 0.9055 | 8.0223 | 15 | 3 | 12.2455 | 4.9107 |
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+ | 0.8094 | 22.0 | 1078 | 0.8579 | 0.659 | 0.4557 | 0.5984 | 0.5982 | 0.9123 | 0.9012 | 0.9063 | 8.0536 | 15 | 3 | 12.3393 | 5.3571 |
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+ | 0.7734 | 23.0 | 1127 | 0.8541 | 0.6576 | 0.4564 | 0.5971 | 0.597 | 0.9116 | 0.9015 | 0.9061 | 8.0848 | 15 | 3 | 12.3705 | 4.9107 |
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+ | 0.775 | 24.0 | 1176 | 0.8490 | 0.661 | 0.4586 | 0.5999 | 0.5993 | 0.912 | 0.9019 | 0.9065 | 8.0759 | 15 | 3 | 12.3125 | 4.9107 |
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+ | 0.7897 | 25.0 | 1225 | 0.8448 | 0.66 | 0.457 | 0.6007 | 0.5997 | 0.9126 | 0.9011 | 0.9064 | 8.0357 | 15 | 3 | 12.2902 | 4.4643 |
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+ | 0.7817 | 26.0 | 1274 | 0.8409 | 0.6584 | 0.4557 | 0.5987 | 0.5982 | 0.9122 | 0.9006 | 0.906 | 7.9955 | 15 | 3 | 12.25 | 4.4643 |
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+ | 0.7839 | 27.0 | 1323 | 0.8362 | 0.6612 | 0.4595 | 0.6015 | 0.601 | 0.9128 | 0.901 | 0.9065 | 7.9911 | 15 | 3 | 12.2545 | 4.4643 |
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+ | 0.7964 | 28.0 | 1372 | 0.8317 | 0.6611 | 0.465 | 0.6048 | 0.604 | 0.9128 | 0.9018 | 0.9069 | 8.067 | 15 | 3 | 12.3393 | 4.4643 |
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+ | 0.7634 | 29.0 | 1421 | 0.8282 | 0.6632 | 0.466 | 0.6052 | 0.6045 | 0.9133 | 0.9022 | 0.9074 | 8.0714 | 16 | 3 | 12.3438 | 4.4643 |
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+ | 0.7939 | 30.0 | 1470 | 0.8250 | 0.6605 | 0.4617 | 0.6025 | 0.6019 | 0.913 | 0.9022 | 0.9072 | 8.0446 | 16 | 3 | 12.3482 | 4.9107 |
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+ | 0.776 | 31.0 | 1519 | 0.8209 | 0.6645 | 0.4668 | 0.6073 | 0.6065 | 0.9133 | 0.9029 | 0.9077 | 8.0938 | 16 | 3 | 12.4062 | 5.8036 |
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+ | 0.7511 | 32.0 | 1568 | 0.8192 | 0.6636 | 0.4652 | 0.6068 | 0.606 | 0.9128 | 0.9029 | 0.9074 | 8.1071 | 16 | 3 | 12.4152 | 6.25 |
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+ | 0.7523 | 33.0 | 1617 | 0.8165 | 0.6638 | 0.4658 | 0.6067 | 0.6063 | 0.9126 | 0.9029 | 0.9073 | 8.1205 | 16 | 3 | 12.4286 | 6.25 |
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+ | 0.7534 | 34.0 | 1666 | 0.8142 | 0.664 | 0.4684 | 0.6087 | 0.6079 | 0.9122 | 0.903 | 0.9072 | 8.1071 | 15 | 3 | 12.4196 | 6.25 |
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+ | 0.7578 | 35.0 | 1715 | 0.8118 | 0.6621 | 0.4633 | 0.6039 | 0.6033 | 0.9117 | 0.9011 | 0.906 | 8.0759 | 15 | 3 | 12.3571 | 5.8036 |
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+ | 0.7687 | 36.0 | 1764 | 0.8094 | 0.6615 | 0.4612 | 0.6035 | 0.6026 | 0.9116 | 0.9008 | 0.9058 | 8.0625 | 15 | 3 | 12.3304 | 5.8036 |
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+ | 0.7423 | 37.0 | 1813 | 0.8075 | 0.6607 | 0.4605 | 0.6028 | 0.6022 | 0.9114 | 0.9009 | 0.9057 | 8.0714 | 15 | 3 | 12.3482 | 5.8036 |
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+ | 0.766 | 38.0 | 1862 | 0.8056 | 0.6593 | 0.4591 | 0.6027 | 0.6021 | 0.9111 | 0.9008 | 0.9055 | 8.0848 | 15 | 3 | 12.3705 | 6.25 |
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+ | 0.7422 | 39.0 | 1911 | 0.8044 | 0.6616 | 0.4605 | 0.6021 | 0.6014 | 0.9109 | 0.901 | 0.9055 | 8.0893 | 16 | 3 | 12.3795 | 5.8036 |
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+ | 0.754 | 40.0 | 1960 | 0.8029 | 0.6629 | 0.4595 | 0.6016 | 0.6012 | 0.9111 | 0.9009 | 0.9055 | 8.0446 | 16 | 3 | 12.3259 | 5.3571 |
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+ | 0.7326 | 41.0 | 2009 | 0.8017 | 0.6637 | 0.4602 | 0.6024 | 0.6018 | 0.911 | 0.9011 | 0.9056 | 8.0625 | 16 | 3 | 12.3482 | 5.3571 |
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+ | 0.7847 | 42.0 | 2058 | 0.8008 | 0.6637 | 0.4602 | 0.6024 | 0.6018 | 0.911 | 0.9011 | 0.9056 | 8.0625 | 16 | 3 | 12.3482 | 5.3571 |
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+ | 0.7426 | 43.0 | 2107 | 0.7997 | 0.664 | 0.4604 | 0.603 | 0.6023 | 0.911 | 0.901 | 0.9055 | 8.0536 | 16 | 3 | 12.3393 | 4.9107 |
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+ | 0.7476 | 44.0 | 2156 | 0.7990 | 0.6666 | 0.4628 | 0.6057 | 0.6051 | 0.9115 | 0.9014 | 0.906 | 8.0357 | 16 | 3 | 12.317 | 4.4643 |
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+ | 0.752 | 45.0 | 2205 | 0.7983 | 0.6666 | 0.4629 | 0.6057 | 0.6053 | 0.9116 | 0.9014 | 0.906 | 8.0312 | 16 | 3 | 12.3125 | 4.4643 |
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+ | 0.7256 | 46.0 | 2254 | 0.7979 | 0.6661 | 0.4623 | 0.6049 | 0.6047 | 0.9115 | 0.901 | 0.9058 | 8.0089 | 16 | 3 | 12.2902 | 4.4643 |
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+ | 0.752 | 47.0 | 2303 | 0.7974 | 0.6642 | 0.4623 | 0.6044 | 0.604 | 0.9111 | 0.9008 | 0.9055 | 8.0312 | 16 | 3 | 12.317 | 4.4643 |
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+ | 0.7503 | 48.0 | 2352 | 0.7971 | 0.6672 | 0.4657 | 0.6067 | 0.6067 | 0.9113 | 0.9013 | 0.9059 | 8.058 | 16 | 3 | 12.3438 | 4.4643 |
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+ | 0.7515 | 49.0 | 2401 | 0.7970 | 0.6672 | 0.4657 | 0.6067 | 0.6067 | 0.9113 | 0.9013 | 0.9059 | 8.058 | 16 | 3 | 12.3438 | 4.4643 |
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+ | 0.7312 | 50.0 | 2450 | 0.7969 | 0.6672 | 0.4657 | 0.6067 | 0.6067 | 0.9113 | 0.9013 | 0.9059 | 8.058 | 16 | 3 | 12.3438 | 4.4643 |
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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
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+ "transformers_version": "4.33.1"
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+ }
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