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anuragrawal
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bart-large-cnn-YT-transcript-sum

Text2Text Generation
Transformers
PyTorch
bart
Generated from Trainer
Inference Endpoints
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End of training

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  1. README.md +23 -23
  2. pytorch_model.bin +1 -1
  3. tokenizer.json +16 -2
  4. training_args.bin +1 -1
README.md CHANGED
@@ -17,12 +17,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.4333
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- - Rouge1: 51.3497
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- - Rouge2: 24.9492
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- - Rougel: 37.3016
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- - Rougelsum: 47.8317
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- - Gen Len: 97.6074
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  ## Model description
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@@ -51,23 +51,23 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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- |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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- | 1.6217 | 1.0 | 540 | 1.4424 | 50.4219 | 24.0335 | 35.9464 | 46.8506 | 90.2630 |
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- | 1.0592 | 2.0 | 1080 | 1.4333 | 51.3497 | 24.9492 | 37.3016 | 47.8317 | 97.6074 |
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- | 0.723 | 3.0 | 1620 | 1.6339 | 50.9967 | 24.488 | 37.1713 | 47.2127 | 98.6370 |
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- | 0.4971 | 4.0 | 2160 | 1.7243 | 51.2797 | 24.0696 | 36.6573 | 47.4137 | 99.8370 |
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- | 0.3559 | 5.0 | 2700 | 1.9144 | 51.4203 | 24.3533 | 36.8606 | 47.6179 | 96.4481 |
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- | 0.2528 | 6.0 | 3240 | 2.1977 | 51.3129 | 24.724 | 36.992 | 47.8757 | 97.7630 |
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- | 0.1804 | 7.0 | 3780 | 2.3279 | 51.3644 | 23.9238 | 36.5924 | 47.2068 | 94.4481 |
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- | 0.1331 | 8.0 | 4320 | 2.4088 | 51.742 | 24.7768 | 37.6327 | 48.0398 | 90.7778 |
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- | 0.0949 | 9.0 | 4860 | 2.5075 | 52.0502 | 24.8043 | 37.5371 | 48.0676 | 86.2037 |
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- | 0.074 | 10.0 | 5400 | 2.5641 | 52.0733 | 25.2822 | 37.6324 | 48.3677 | 93.3370 |
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- | 0.0506 | 11.0 | 5940 | 2.7945 | 52.2919 | 25.5404 | 37.956 | 48.4453 | 93.2222 |
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- | 0.0343 | 12.0 | 6480 | 2.8614 | 52.0782 | 25.039 | 37.5952 | 48.469 | 89.5222 |
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- | 0.0126 | 13.0 | 7020 | 3.0071 | 52.3343 | 25.4229 | 37.8453 | 48.4345 | 92.5889 |
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- | 0.0065 | 14.0 | 7560 | 3.0398 | 52.0812 | 24.9641 | 37.409 | 48.2338 | 94.3037 |
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- | 0.0032 | 15.0 | 8100 | 3.0438 | 52.184 | 25.1639 | 37.4424 | 48.3197 | 93.2333 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.4849
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+ - Rouge1: 48.0422
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+ - Rouge2: 22.8938
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+ - Rougel: 34.0775
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+ - Rougelsum: 44.7056
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+ - Gen Len: 108.8009
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
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+ | No log | 1.0 | 432 | 1.5362 | 49.0506 | 22.9422 | 35.5667 | 45.7219 | 88.0602 |
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+ | 1.5312 | 2.0 | 864 | 1.4849 | 48.0422 | 22.8938 | 34.0775 | 44.7056 | 108.8009 |
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+ | 0.9026 | 3.0 | 1296 | 1.5761 | 50.0558 | 23.9657 | 36.247 | 46.4508 | 96.0231 |
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+ | 0.5642 | 4.0 | 1728 | 1.8304 | 50.6862 | 24.4638 | 36.3568 | 47.2607 | 93.1667 |
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+ | 0.3629 | 5.0 | 2160 | 1.9355 | 51.2362 | 25.1077 | 37.772 | 47.4362 | 88.9583 |
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+ | 0.2335 | 6.0 | 2592 | 2.1215 | 49.5831 | 23.4294 | 35.9861 | 45.9306 | 94.2917 |
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+ | 0.1603 | 7.0 | 3024 | 2.2890 | 49.8716 | 23.4756 | 36.2617 | 46.2866 | 88.7639 |
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+ | 0.1603 | 8.0 | 3456 | 2.3604 | 49.5627 | 23.6399 | 35.9596 | 45.7914 | 88.8333 |
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+ | 0.1049 | 9.0 | 3888 | 2.5252 | 50.358 | 24.1986 | 36.5297 | 46.5519 | 90.5463 |
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+ | 0.0744 | 10.0 | 4320 | 2.6694 | 50.46 | 24.1493 | 37.0205 | 46.8988 | 91.0139 |
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+ | 0.049 | 11.0 | 4752 | 2.7840 | 50.8805 | 24.5482 | 36.5901 | 46.9176 | 90.8380 |
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+ | 0.0312 | 12.0 | 5184 | 2.8330 | 50.4793 | 24.6444 | 37.2087 | 46.7151 | 86.9444 |
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+ | 0.0156 | 13.0 | 5616 | 2.9540 | 50.3911 | 24.4843 | 36.8037 | 46.8691 | 94.9352 |
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+ | 0.0083 | 14.0 | 6048 | 3.0214 | 51.0557 | 25.127 | 37.1368 | 47.3072 | 92.5787 |
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+ | 0.0083 | 15.0 | 6480 | 3.0340 | 51.3998 | 25.5847 | 37.5635 | 47.7132 | 90.5602 |
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  ### Framework versions
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