update model card README.md

README.md

@@ -1,11 +1,79 @@
 ---
-language:
-- en
+license: apache-2.0
 tags:
-- summarization
-license: mit
+- generated_from_trainer
 datasets:
 - multi_news
 metrics:
 - rouge
----
+model-index:
+- name: distilbart-cnn-12-6-ftn-multi_news
+  results:
+  - task:
+      name: Sequence-to-sequence Language Modeling
+      type: text2text-generation
+    dataset:
+      name: multi_news
+      type: multi_news
+      args: default
+    metrics:
+    - name: Rouge1
+      type: rouge
+      value: 39.9832
+---
+
+<!-- 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. -->
+
+# distilbart-cnn-12-6-ftn-multi_news
+
+This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the multi_news dataset.
+It achieves the following results on the evaluation set:
+- Loss: 4.0202
+- Rouge1: 39.9832
+- Rouge2: 13.0653
+- Rougel: 22.1761
+- Rougelsum: 34.5466
+- Gen Len: 132.41
+
+## 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: 2e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 1
+- mixed_precision_training: Native AMP
+- label_smoothing_factor: 0.1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
+| 4.1427        | 0.89  | 400  | 4.0202          | 39.9832 | 13.0653 | 22.1761 | 34.5466   | 132.41  |
+
+
+### Framework versions
+
+- Transformers 4.18.0
+- Pytorch 1.11.0
+- Datasets 2.1.0
+- Tokenizers 0.12.1