Update README.md

README.md

@@ -1,13 +1,21 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- flattery
+- business calls
+- speech
+language:
+- en
+pipeline_tag: audio-classification
+inference: false
 ---
 
-# Model Card for Model ID
+# Flattery Prediction from Speech
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-
+This Wav2Vec2 model was finetuned to predict **flattery from speech** English **earning calls**. It was introduced in [this paper](#), which was accepted at INTERSPEECH 2024.
+If you are looking for the text-based classifier (based on RoBERTa) introduced in the paper, please see [here](https://huggingface.co/chrlukas/flattery_prediction_text).
 
 ## Model Details
 
@@ -15,187 +23,58 @@ tags: []
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This is a (further) fine-tuned variant of a [Wav2Vec2 model for Speech Emotion Recognition in MSP](https://huggingface.co/audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim). 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, 3.2.2 for the training procedure and 4.2 for the results. The checkpoint provided here was trained without further pruning the model. 
+It achieves Unweighed Average Recall (UAR) values of .8001 and .8084 on the development and test partition, respectively. 
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
+### Model Sources
 
 <!-- Provide the basic links for the model. -->
 
 - **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
+## Uses
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+The following snippet illustrates the usage of the model. 
+```python
+from transformers import AutoFeatureExtractor, Wav2Vec2ForSequenceClassification
+from torch import sigmoid
+import librosa
+
+# initialize model and tokenizer
+checkpoint = "chrlukas/flattery_prediction_speech"
+processor = AutoFeatureExtractor.from_pretrained(checkpoint)
+model = Wav2Vec2ForSequenceClassification.from_pretrained(checkpoint)
+
+# predict flattery in a sentence
+example_file = 'example.wav'
+# audio must be resampled to 16Hz
+y, _ = librosa.load(test_file, sr=16000)
+inp = processor(y, sampling_rate=16000, return_tensors='pt')
+logits = model(**inp).logits
+prediction = sigmoid(logits).item()
+flattery = prediction >= 0.5
+print(f'Flattery detected? {flattery}')
+```
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+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. Moreover, the majority of speakers (162/178) in the training dataset are male. However, we found this to have rather little impact on the model's performance for 
+held-out female speakers (cf. Section 4.4 in the paper)
 
-[More Information Needed]
 
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
+## 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:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
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-
-## Model Card Authors [optional]
-
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-
-## Model Card Contact
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-
-
+TODO