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led-base-16384-biolaysum-both-with_references

Text2Text Generation
Transformers
PyTorch
led
generated_from_trainer
Inference Endpoints
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update model card README.md

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@@ -16,11 +16,11 @@ should probably proofread and complete it, then remove this comment. -->
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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 None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.2428
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- - Rouge1: 0.4562
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- - Rouge2: 0.1529
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- - Rougel: 0.2402
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- - Rougelsum: 0.2401
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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 |
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- |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
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- | 2.4777 | 0.14 | 1000 | 2.3948 | 0.4284 | 0.1431 | 0.2336 | 0.2336 |
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- | 2.3863 | 0.27 | 2000 | 2.3211 | 0.4455 | 0.1496 | 0.2380 | 0.2379 |
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- | 2.3509 | 0.41 | 3000 | 2.2809 | 0.4521 | 0.1521 | 0.2406 | 0.2406 |
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- | 2.3063 | 0.55 | 4000 | 2.2428 | 0.4562 | 0.1529 | 0.2402 | 0.2401 |
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- | 2.2754 | 0.69 | 5000 | 2.2222 | 0.4491 | 0.1506 | 0.2393 | 0.2393 |
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- | 2.268 | 0.82 | 6000 | 2.2113 | 0.4499 | 0.1519 | 0.2406 | 0.2405 |
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- | 2.2594 | 0.96 | 7000 | 2.1892 | 0.4519 | 0.1515 | 0.2390 | 0.2391 |
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  ### Framework versions
 
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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 None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.1595
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+ - Rouge1: 0.4548
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+ - Rouge2: 0.1555
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+ - Rougel: 0.2435
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+ - Rougelsum: 0.2435
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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 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
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+ | 2.2751 | 0.69 | 5000 | 2.2219 | 0.4488 | 0.1496 | 0.2392 | 0.2392 |
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+ | 2.0407 | 1.37 | 10000 | 2.1595 | 0.4548 | 0.1555 | 0.2435 | 0.2435 |
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+ | 1.9246 | 2.06 | 15000 | 2.1263 | 0.4537 | 0.1522 | 0.2395 | 0.2396 |
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+ | 1.9066 | 2.75 | 20000 | 2.1091 | 0.4562 | 0.1538 | 0.2409 | 0.2409 |
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+ | 1.7802 | 3.43 | 25000 | 2.0998 | 0.4539 | 0.1523 | 0.2411 | 0.2411 |
 
 
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  ### Framework versions