Clinc OOS Intent Classifier

This model is designed for classifying the intent of user utterances in conversations and is trained on the CLINC OOS dataset. It can identify the user's intention behind various questions and requests.

Model

This model is a fine-tuned version of the pre-trained google-bert/bert-base-multilingual-cased model. This base model was chosen for its ability to understand multiple languages and its strong performance in text classification tasks.

Intended uses

This model is suitable for the following applications:

Automated Customer Support Systems: Identifying user intent to route inquiries and provide relevant responses.
Conversation Analysis: Categorizing user questions and requests to better understand user needs and issues.
Improving User Experience: Quickly identifying user needs to deliver better services.
Research in NLP: Serving as a baseline model for developing natural language understanding systems.

Factors

The performance of this model may be affected by the following factors:

Diversity of Input Data: The model is trained on the CLINC OOS dataset and may exhibit weaker performance when encountering phrases and sentences outside of this domain.
Language Variations: Although the model is based on a multilingual model, its performance may vary across languages, especially those with less training data.
Complexity and Ambiguity of Utterances: Complex or ambiguous phrases may reduce the model's accuracy.

Metrics

The model's performance is evaluated using the Accuracy metric on the evaluation dataset. Accuracy indicates the proportion of the model's predictions that match the actual labels.

Evaluation results

Based on the evaluation on the validation set, the model achieved an accuracy of 93.6%. Please note that the model's performance in real-world applications may vary.

How to use

To use this model, you can utilize the transformers library. Here is a sample Python code snippet for using the model:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "frameai/ClincOOS"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

def predict_intent(text):
    inputs = tokenizer(text, return_tensors="pt")
    outputs = model(**inputs)
    predictions = outputs.logits.argmax().item()
    return model.config.id2label[predictions]

text_to_predict = "Play some music"
predicted_intent = predict_intent(text_to_predict)
print(f"Text: {text_to_predict}, Intent: {predicted_intent}")

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ClincOOS