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--- |
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license: apache-2.0 |
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datasets: google-research-datasets/go_emotions |
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base_model: FacebookAI/xlm-roberta-base |
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language: |
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- de |
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metrics: |
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- f1_macro: 0.45 |
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- accuracy: 0.41 |
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- kappa: 0.42 |
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pipeline_tag: text-classification |
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tags: |
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- medical |
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model_description: >- |
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This is basically the German translation of arpanghoshal/EmoRoBERTa. We used |
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the go_emotions dataset, translated it into German and fine-tuned the |
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FacebookAI/xlm-roberta-base model. So this model allows the classification |
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of 28 emotions in German Transcripts ('admiration', 'amusement', 'anger', |
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'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', |
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'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', |
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'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', |
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'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', |
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'neutral'). A paper will be published soonish... |
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--- |
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# Model Card for G-E5-rman-Emotions |
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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... |
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## Model Details |
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- **Model type:** text-classification |
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- **Language(s) (NLP):** German |
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- **License:** apache-2.0 |
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- **Finetuned from model:** intfloat/multilingual-e5-large |
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- **Hyperparameters:** |
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- Epochs: 10 |
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- learning_rate: 3e-5 |
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- weight_decay: 0.01 |
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- **Metrics:** |
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- accuracy: 0.41 |
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- f1: 0.45 |
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- kappa: 0.42 |
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--- |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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# pip install transformers[torch] |
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# pip install pandas, transformers, numpy, tqdm, openpyxl |
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import pandas as pd |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer |
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import numpy as np |
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from tqdm import tqdm |
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import time |
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import os |
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from transformers import DataCollatorWithPadding |
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import json |
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# create base path and input and output path for the model folder and the file folder |
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base_path = "/share/users/staff/c/clalk/Emotionen" |
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model_path = os.path.join(base_path, 'Modell') |
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file_path = os.path.join(base_path, 'Datensatz') |
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MODEL = "intfloat/multilingual-e5-large" |
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tokenizer = AutoTokenizer.from_pretrained(MODEL, do_lower_case=False) |
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model = AutoModelForSequenceClassification.from_pretrained( |
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model_path, |
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from_tf=False, |
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from_flax=False, |
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trust_remote_code=False, |
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num_labels=28, |
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ignore_mismatched_sizes=True |
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) |
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data_collator = DataCollatorWithPadding(tokenizer=tokenizer) |
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# Path to the file |
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os.chdir(file_path) |
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df_full = pd.read_excel("speech_turns_pat.xlsx", index_col=None) |
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if 'Unnamed: 0' in df_full.columns: |
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df_full = df_full.drop(columns=['Unnamed: 0']) |
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df_full.reset_index(drop=True, inplace=True) |
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# Tokenization and inference function |
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def infer_texts(texts): |
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tokenized_texts = tokenizer(texts, return_tensors="pt", padding=True, truncation=True) |
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class SimpleDataset: |
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def __init__(self, tokenized_texts): |
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self.tokenized_texts = tokenized_texts |
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def __len__(self): |
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return len(self.tokenized_texts["input_ids"]) |
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def __getitem__(self, idx): |
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return {k: v[idx] for k, v in self.tokenized_texts.items()} |
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test_dataset = SimpleDataset(tokenized_texts) |
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trainer = Trainer(model=model, data_collator=data_collator) |
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predictions = trainer.predict(test_dataset) |
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sigmoid = torch.nn.Sigmoid() |
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probs = sigmoid(torch.Tensor(predictions.predictions)) |
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return np.round(np.array(probs), 3).tolist() |
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start_time = time.time() |
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df = df_full |
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# Save results in a dict |
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results = [] |
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for index, row in tqdm(df.iterrows(), total=df.shape[0]): |
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patient_texts = row['Patient'] |
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prob_list = infer_texts(patient_texts) |
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results.append({ |
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"File": row['Class']+"_"+row['session'], |
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"Class": row['Class'], |
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"session": row['session'], |
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"short_id": row["short_id"], |
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"long_id": row["long_id"], |
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"Sentence": patient_texts, |
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"Prediction": prob_list[0], |
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"hscl-11": row["Gesamtscore_hscl"], |
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"srs": row["srs_ges"], |
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}) |
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# Convert results to df |
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df_results = pd.DataFrame(results) |
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df_results.to_json("emo_speech_turn_inference.json") |
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end_time = time.time() |
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elapsed_time = end_time - start_time |
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print(f"Elapsed time: {elapsed_time:.2f} seconds") |
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print(df_results) |
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emo_df = pd.DataFrame(df_results['Prediction'].tolist(), index=df_results["Class"].index) |
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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'] |
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emo_df.columns = col_names |
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print(emo_df) |
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``` |