Create README.md

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+### How to use the model
+
+```python 
+import pandas as pd
+import numpy as np
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load model
+model = AutoModelForSequenceClassification.from_pretrained("lkonle/EMO_Sadness_gbert")
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("lkonle/EMO_Sadness_gbert")
+tokenizer.pad_token = "[PAD]"
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+
+# define input text
+myinput = ["Paul war sehr sehr glücklich über seinen Welpen.",
+           "Paul war sehr traurig über sein Frühstück.",
+           "Paul hatte große Langeweile."]
+
+# tokenize, encode, format as batch and return pytorch tensors
+input_ids = tokenizer.batch_encode_plus(myinput, truncation=True, padding="max_length", padding_side="right", return_tensors="pt")
+
+# predict
+logits = model(**input_ids)["logits"]
+
+# get the predicted label
+result = logits.detach().numpy()
+prediction = np.argmax(result, axis=1)
+
+# store result in pandas
+output = pd.DataFrame()
+output["inputs"] =  myinput
+output["prediction"] = prediction
+print(output)
+```