Update README.md

README.md

@@ -24,7 +24,7 @@ model_description: >-
 ---
 # Model Card for G-E5-rman-Emotions
 
-This is basically the German translation of arpanghoshal/EmoRoBERTa. We used the go_emotions dataset, translated it into German and fine-tuned the intfloat/multilingual-e5-large model. So this model allows the classification of **28 emotions** in German Transcripts (**'admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'**). A paper will be published soonish...
+This is basically the German translation of arpanghoshal/EmoRoBERTa. We used the go_emotions dataset, translated it into German and fine-tuned the FacebookAI/xlm-roberta-base model. So this model allows the classification of **28 emotions** in German Transcripts (**'admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'**). A paper will be published soonish...
 
 
 ## Model Details
@@ -67,7 +67,7 @@ base_path = "/share/users/staff/c/clalk/Emotionen"
 model_path = os.path.join(base_path, 'Modell')
 file_path = os.path.join(base_path, 'Datensatz')
 
-MODEL = "intfloat/multilingual-e5-large"
+MODEL = "FacebookAI/xlm-roberta-base"
 tokenizer = AutoTokenizer.from_pretrained(MODEL, do_lower_case=False)
 model = AutoModelForSequenceClassification.from_pretrained(
     model_path,
@@ -108,7 +108,8 @@ def infer_texts(texts):
 start_time = time.time()
 df = df_full
 
-# Save results in a dict
+# Save results in a dict, here the df contains the additional variables File, Class, session, short_id, long_id, Prediction, hscl-11, and srs.
+# However, only the "Sentence" column with the text is relevant for the pipeline. 
 results = []
 for index, row in tqdm(df.iterrows(), total=df.shape[0]):
     patient_texts = row['Patient']