biunlp
/

mLongT5HeSum-base

@@ -1,201 +1,81 @@
 ---
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 ---
+license: apache-2.0
+base_model: agemagician/mlong-t5-tglobal-base
+tags:
+- generated_from_trainer
+metrics:
+- rouge
+model-index:
+- name: mlong-t5-tglobal-base
+  results: []
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# mlong-t5-tglobal-base
+This model is a fine-tuned version of [agemagician/mlong-t5-tglobal-base](https://huggingface.co/agemagician/mlong-t5-tglobal-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.1553
+- Rouge1: 32.0603
+- Rouge2: 13.4985
+- Rougel: 24.0775
+- Rougelsum: 25.9692
+- Gen Len: 72.828
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 1
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 30
+### Training results
+| Training Loss | Epoch | Step  | Gen Len | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
+|:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:|:-------:|:-------:|:---------:|
+| No log        | 1.0   | 500   | 18.987  | 2.2709          | 20.5043 | 8.1518  | 16.9526 | 17.5001   |
+| 2.8714        | 2.0   | 1000  | 18.982  | 2.2022          | 21.4051 | 8.7445  | 17.7534 | 18.3191   |
+| 2.8714        | 3.0   | 1500  | 18.99   | 2.1608          | 21.6609 | 9.1753  | 18.0374 | 18.6176   |
+| 2.5137        | 4.0   | 2000  | 18.993  | 2.1555          | 21.6818 | 9.1814  | 18.0382 | 18.6198   |
+| 2.5137        | 5.0   | 2500  | 18.994  | 2.1462          | 21.9708 | 9.2033  | 18.3919 | 18.9535   |
+| 2.3717        | 6.0   | 3000  | 18.996  | 2.1258          | 22.0583 | 9.2987  | 18.4379 | 19.0322   |
+| 2.3717        | 7.0   | 3500  | 18.989  | 2.1278          | 21.8245 | 9.0474  | 18.1979 | 18.8038   |
+| 2.2633        | 8.0   | 4000  | 18.996  | 2.1207          | 21.6273 | 8.8847  | 18.024  | 18.6049   |
+| 2.2633        | 9.0   | 4500  | 18.994  | 2.1180          | 22.2004 | 9.6253  | 18.6373 | 19.1721   |
+| 2.1886        | 10.0  | 5000  | 18.988  | 2.1220          | 22.1619 | 9.6206  | 18.5069 | 19.0856   |
+| 2.1886        | 11.0  | 5500  | 18.987  | 2.1161          | 22.1518 | 9.4522  | 18.4695 | 19.0552   |
+| 2.1144        | 12.0  | 6000  | 18.995  | 2.1103          | 22.0395 | 9.4185  | 18.4314 | 19.0305   |
+| 2.1144        | 13.0  | 6500  | 18.992  | 2.1150          | 22.2404 | 9.4722  | 18.5482 | 19.1747   |
+| 2.054         | 14.0  | 7000  | 19.0    | 2.1091          | 22.1466 | 9.3434  | 18.3443 | 18.9233   |
+| 2.0526        | 1.0   | 8000  | 62.488  | 2.1580          | 30.4149 | 12.0774 | 22.9493 | 24.4478   |
+| 2.1236        | 2.0   | 16000 | 64.797  | 2.1621          | 31.3101 | 13.3237 | 23.8249 | 25.526    |
+| 2.0776        | 3.0   | 24000 | 57.059  | 2.1607          | 30.9902 | 12.3753 | 23.0243 | 24.8308   |
+| 1.9843        | 4.0   | 32000 | 72.828  | 2.1553          | 32.0603 | 13.4985 | 24.0775 | 25.9692   |
+### Framework versions
+- Transformers 4.38.2
+- Pytorch 1.13.1+cu117
+- Datasets 2.18.0
+- Tokenizers 0.15.2