Update README.md

README.md

@@ -1,5 +1,13 @@
 ---
+language: 
+- en
+- fr
+- ro
+- de
+datasets:
+- c4
 library_name: transformers
+
 license: apache-2.0
 ---
 
@@ -21,15 +29,15 @@ to EncT5:
 1. There are less decoder layers (a single decoder layer by default), and so has fewer parameters/resources than the
    standard T5.
 3. There is a separate decoder word embedding, with the decoder input ids being predefined constants. During
-   fine-tuning, these constants are trained to effectively "prompt" the encoder to perform the necessary
+   fine-tuning, the decoder embedding learns to use these constants as "prompts" to the encoder for the corresponding
    classification/regression tasks.
-4. There is a classification head on top of the decoder output.
+5. There is a classification head on top of the decoder output.
 
 Research has shown that this model can be more efficient and usable over T5 and BERT for non-autoregressive
 tasks such as classification and regression.
 
-- **Developed by:** Frederick Liu, Terry Huang, Shihang Lyu, Siamak Shakeri, Hongkun Yu, Jing Li. See
-  [associated paper](https://arxiv.org/abs/2110.08426)
+- **Developed by:** Frederick Liu, Terry Huang, Shihang Lyu, Siamak Shakeri, Hongkun Yu, Jing Li. See the
+  [associated paper](https://arxiv.org/abs/2110.08426).
 - **Model type:** Language Model
 - **Language(s) (NLP):** English, French, Romanian, German
 - **License:** Apache 2.0
@@ -41,8 +49,10 @@ tasks such as classification and regression.
 
 Use the code below to get started with the model.
 
-  model = AutoModelForSequenceClassification.from_pretrained("hackyon/enct5-base", trust_remote_code=True)
-  # Fine-tune the model before use.
+```python
+model = AutoModelForSequenceClassification.from_pretrained("hackyon/enct5-base", trust_remote_code=True)
+# Fine-tune the model before use.
+```
 
 See the [github repro](https://github.com/hackyon/EncT5) for a more comprehensive guide.