Update README.md

README.md

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 license: apache-2.0
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+# Model Card for transition-physical
+
+## Model Description
+
+This is the fine-tuned ClimateBERT language model with a classification head for detecting sentences that are either related to transition risks or to physical climate risks.
+Using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model as starting point, the distilroberta-base-climate-detector model is fine-tuned on our human-annotated dataset.
+ 
+## Citation Information
+
+```bibtex
+@article{deng2023war,
+  title={War and Policy: Investor Expectations on the Net-Zero Transition},
+  author={Deng, Ming and Leippold, Markus and Wagner, Alexander F and Wang, Qian},
+  journal={Swiss Finance Institute Research Paper},
+  number={22-29},
+  year={2023}
+}
+```
+
+## 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 = “"climatebert/distilroberta-base-climate-detector"
+model_name = "climatebert/transition-physical"
+ 
+# If you want to use your own data, simply load them as 🤗 Datasets dataset, see https://huggingface.co/docs/datasets/loading
+dataset = datasets.load_dataset(dataset_name, split="test")
+ 
+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
+for out in tqdm(pipe(KeyDataset(dataset, "text"), padding=True, truncation=True)):
+   print(out)
+```