Update README.md

README.md

@@ -1,23 +1,22 @@
 ---
-language:
-- it_IT
-- it_IT
 tags:
-- generated_from_trainer
+- summarization
+language:
+- it
 metrics:
 - rouge
 model-index:
 - name: summarization_mbart_mlsum
   results: []
+datasets:
+- ARTeLab/mlsum-it
 ---
 
-<!-- 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. -->
+# mbart_summarization_mlsum
 
-# summarization_mbart_mlsum
+This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on mlsum-it for Abstractive Summarization.
 
-This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on an unknown dataset.
-It achieves the following results on the evaluation set:
+It achieves the following results:
 - Loss: 3.3336
 - Rouge1: 19.3489
 - Rouge2: 6.4028
@@ -25,19 +24,13 @@ It achieves the following results on the evaluation set:
 - Rougelsum: 16.5387
 - Gen Len: 33.5945
 
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
+## Usage 
 
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+```python
+from transformers import MBartTokenizer, MBartForConditionalGeneration
+tokenizer = MBartTokenizer.from_pretrained("ARTeLab/mbart-summarization-mlsum")
+model = MBartForConditionalGeneration.from_pretrained("ARTeLab/mbart-summarization-mlsum")
+```
 
 ### Training hyperparameters
 
@@ -50,13 +43,9 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 4.0
 
-### Training results
-
-
-
 ### Framework versions
 
 - Transformers 4.15.0.dev0
 - Pytorch 1.10.0+cu102
 - Datasets 1.15.1
-- Tokenizers 0.10.3
+- Tokenizers 0.10.3