---
library_name: transformers
tags:
- flattery
- business calls
language:
- en
pipeline_tag: text-classification
inference: false
---

# Flattery Prediction from Text

<!-- Provide a quick summary of what the model is/does. -->

This model was finetuned to predict **flattery** in transcripts of English **earning calls**. It was introduced in [This Paper Had the Smartest Reviewers -- Flattery Detection Utilising an
  Audio-Textual Transformer-Based Approach](http://arxiv.org/abs/2406.17667), which was accepted at INTERSPEECH 2024. 

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is a fine-tuned variant of [RoBERTa-base](https://huggingface.co/FacebookAI/roberta-base). It is trained using a dataset comprising single sentences uttered in business calls, 
which were labeled for flattery in a binary manner. The training set comprised 7167 sentences, 1878 sentences were used as development set. For more details, please 
refer to [the paper(TODO)](#), especially Sections 2 for the dataset, 3.1 for the training procedure and 4.1 for the results. The checkpoint provided here was trained using 
human *gold-standard* transcripts. It achieves Unweighed Average Recall (UAR) values of .8512 and .8865 on the development and test partition, respectively. 


### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]


## Uses

The following snippet illustrates the usage of the model. 
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from torch import sigmoid
import torch

# initialize model and tokenizer
checkpoint = "chrlukas/flattery_prediction_text"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
model.eval()

# predict flattery in a sentence
example = 'This is a great example!'    # should predict flattery
tokenized = tokenizer(example, return_tensors='pt')
with torch.no_grad():
  logits = model(**tokenized).logits
prediction = sigmoid(logits).item()
flattery = prediction >= 0.5
print(f'Flattery detected? {flattery}')
```


## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The model is trained on a highly-domain specific dataset sourced from earning calls, i.e., typically conversations between business analysts and CEOs of US-American companies. Hence, it can not be expected to generalize well to other
domains and contexts. 


## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

TODO