Update README.md

README.md

@@ -25,105 +25,4 @@ This is basically the German translation of arpanghoshal/EmoRoBERTa. We used the
   - roc_auc: 0.658
 
 
-**Example Code**
-"""**Inference**"""
-# pip install transformers[torch]
-# pip install pandas, transformers, numpy, tqdm, openpyxl
 
-import pandas as pd
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer
-import numpy as np
-from tqdm import tqdm
-import time
-import os
-from transformers import DataCollatorWithPadding
-import json
-
-# create base path
-base_path = "/share/users/staff/c/clalk/Emotionen"
-
-#create input and output path for the model folder and the file folder
-model_path = os.path.join(base_path, 'Modell')
-file_path = os.path.join(base_path, 'Datensatz')
-
-
-MODEL = "intfloat/multilingual-e5-large"
-tokenizer = AutoTokenizer.from_pretrained(MODEL, do_lower_case=False)
-model = AutoModelForSequenceClassification.from_pretrained(
-    model_path,
-    from_tf=False,
-    from_flax=False,
-    trust_remote_code=False,
-    num_labels=28,
-    ignore_mismatched_sizes=True
-)
-
-data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
-
-# Path to the file
-os.chdir(file_path)
-
-df_full = pd.read_excel("speech_turns_pat.xlsx", index_col=None)
-
-if 'Unnamed: 0' in df_full.columns:
-    df_full = df_full.drop(columns=['Unnamed: 0'])
-
-
-df_full.reset_index(drop=True, inplace=True)
-
-
-# Funktion zur Tokenisierung und Inferenz
-def infer_texts(texts):
-    tokenized_texts = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
-    class SimpleDataset:
-        def __init__(self, tokenized_texts):
-            self.tokenized_texts = tokenized_texts
-        def __len__(self):
-            return len(self.tokenized_texts["input_ids"])
-        def __getitem__(self, idx):
-            return {k: v[idx] for k, v in self.tokenized_texts.items()}
-    test_dataset = SimpleDataset(tokenized_texts)
-    trainer = Trainer(model=model, data_collator=data_collator)
-    predictions = trainer.predict(test_dataset)
-    sigmoid = torch.nn.Sigmoid()
-    probs = sigmoid(torch.Tensor(predictions.predictions))
-
-    return np.round(np.array(probs), 3).tolist()
-
-start_time = time.time()
-
-df = df_full
-
-# Save results in a dict
-results = []
-for index, row in tqdm(df.iterrows(), total=df.shape[0]):
-    patient_texts = row['Patient']
-    prob_list = infer_texts(patient_texts)
-    results.append({
-        "File": row['Class']+"_"+row['session'],
-        "Class": row['Class'],
-        "session": row['session'],
-        "short_id": row["short_id"],
-        "long_id": row["long_id"],
-        "Sentence": patient_texts,
-        "Prediction": prob_list[0],
-        "hscl-11": row["Gesamtscore_hscl"],
-        "srs": row["srs_ges"],
-    })
-
-# Convert results to df
-df_results = pd.DataFrame(results)
-df_results.to_json("emo_speech_turn_inference.json")
-
-end_time = time.time()
-elapsed_time = end_time - start_time
-print(f"Elapsed time: {elapsed_time:.2f} seconds")
-print(df_results)
-
-
-emo_df = pd.DataFrame(df_results['Prediction'].tolist(), index=df_results["Class"].index)
-col_names = ['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']
-emo_df.columns = col_names
-
-print(emo_df)