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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 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
Model type:
text-classification
Language(s) (NLP):
German
License:
apache-2.0
Finetuned from model:
FacebookAI/xlm-roberta-base
Hyperparameters:
Epochs: 10
learning_rate: 3e-5
weight_decay: 0.01
Metrics:
accuracy: 0.41
f1: 0.45
kappa: 0.42
How to Get Started with the Model
Use the code below to get started with the model.
# pip install transformers[torch]# pip install pandas, transformers, numpy, tqdm, openpyxlimport 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 and input and output path for the model folder and the file folder
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 = "FacebookAI/xlm-roberta-base"
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)
# Tokenization and inference functiondefinfer_texts(texts):
tokenized_texts = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
classSimpleDataset:
def__init__(self, tokenized_texts):
self.tokenized_texts = tokenized_texts
def__len__(self):
returnlen(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, 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']
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)
Runs of ChrisLalk German-Emotions on huggingface.co
1.1K
Total runs
27
24-hour runs
53
3-day runs
59
7-day runs
-220
30-day runs
More Information About German-Emotions huggingface.co Model
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German-Emotions huggingface.co is an online trial and call api platform, which integrates German-Emotions's modeling effects, including api services, and provides a free online trial of German-Emotions, you can try German-Emotions online for free by clicking the link below.
ChrisLalk German-Emotions online free url in huggingface.co:
German-Emotions is an open source model from GitHub that offers a free installation service, and any user can find German-Emotions on GitHub to install. At the same time, huggingface.co provides the effect of German-Emotions install, users can directly use German-Emotions installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
