Update README.md

README.md

@@ -14,9 +14,30 @@ This is the EnvRoBERTa-environmental language model. A language model that is tr
 
 Using the [EnvRoBERTa-base](https://huggingface.co/ESGBERT/EnvRoBERTa-base) model as a starting point, the EnvRoBERTa-environmental Language Model is additionally fine-trained on a 2k environmental dataset to detect environmental text samples.
 
-Get started:
+## How to Get Started With the Model
+You can use the model with a pipeline for text classification:
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+from transformers.pipelines.pt_utils import KeyDataset
+import datasets
+from tqdm.auto import tqdm
+ 
+dataset_name = "climatebert/climate_detection"
+tokenizer_name = "ESGBERT/EnvRoBERTa-environmental"
+model_name = "ESGBERT/EnvRoBERTa-environmental"
+ 
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512)
+ 
+pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0)
+ 
+# See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline
+print(pipe("Scope 1 emissions are reported here on a like-for-like basis against the 2013 baseline and exclude emissions from additional vehicles used during repairs."))
+```
+
+## More details can be found in the paper
 
-More details can be found in the paper:
 ```bibtex
 @article{Schimanski23ESGBERT,
     title={{Bridiging the Gap in ESG Measurement: Using NLP to Quantify Environmental, Social, and Governance Communication}},