update model card README.md

README.md

@@ -1,27 +1,79 @@
 ---
+license: apache-2.0
+tags:
+- generated_from_trainer
 datasets:
 - reddit
 metrics:
 - rouge
+model-index:
+- name: distilbart-cnn-6-6-reddit
+  results:
+  - task:
+      name: Sequence-to-sequence Language Modeling
+      type: text2text-generation
+    dataset:
+      name: reddit
+      type: reddit
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Rouge1
+      type: rouge
+      value: 0.1849
 ---
 
-[Distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) finetuned on the [reddit dataset](https://huggingface.co/datasets/reddit)
+<!-- 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. -->
 
-Example usage:
+# distilbart-cnn-6-6-reddit
 
-```python
-# Load finetuned model
-tokenizer = BartTokenizer.from_pretrained("NielsV/distilbart-cnn-6-6-reddit")
-model = BartForConditionalGeneration.from_pretrained("NielsV/distilbart-cnn-6-6-reddit")
+This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on the reddit dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.9883
+- Rouge1: 0.1849
+- Rouge2: 0.0437
+- Rougel: 0.1273
+- Rougelsum: 0.1601
 
-input_text = "..."  # The text you want summarized
+## Model description
 
-# Tokenize the text, summarize and decode the result 
-inputs = tokenizer(input_txt, max_length=1024, return_tensors="pt")
-summary_ids = model.generate(inputs["input_ids"], num_beams=2, min_length=0, max_length=60)
-summary = tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+More information needed
 
-# The string summary contains the tldr
-```
+## Intended uses & limitations
 
-For more information, check out [this repository](https://github.com/VerleysenNiels/arxiv-summarizer)
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step   | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
+|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|
+| 3.13          | 1.0   | 238116 | 3.2736          | 0.1773 | 0.0392 | 0.1223 | 0.1539    |
+| 2.8586        | 2.0   | 476232 | 3.0449          | 0.1846 | 0.0431 | 0.127  | 0.1601    |
+| 2.7844        | 3.0   | 714348 | 2.9883          | 0.1849 | 0.0437 | 0.1273 | 0.1601    |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.1+cu116
+- Datasets 2.8.0
+- Tokenizers 0.13.2