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color54/
roberta-base-blendx2

Name: roberta-base-blendx2
Availability: InStock

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RoBERTa-base-BlendX2 model

Model Details

This model is a fine-tuned version of the RoBERTa-base designed for a multi-intent detection task. The training was conducted using a subset of the BlendX dataset, specifically containing data with exactly two intents per sample. This setup enables the model to handle multi-intent classification, where the goal is to predict both intents for each input.

Uses

This model is designed specifically for the downstream task of multi-intent classification, where the objective is to identify multiple intents within a single input. It is particularly suited for scenarios involving two-intents detection, as it was fine-tuned on a dataset containing samples with exactly two intents.

How to Get Started with the Model

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("color54/roberta-base-blendx2")
model = AutoModelForSequenceClassification.from_pretrained("color54/roberta-base-blendx2")

Training Details

Training Data

BlendX: A multi-intent detection benchmark designed to address the limitations of existing datasets like MixATIS and MixSNIPS by incorporating more complex and diverse utterances. Only utterances with two intents are used.

Training Hyperparameters

Epochs
Batch size
Learning rate
Weight decay
Classification threshold

Evaluation

Testing Data

BlendX: A multi-intent detection benchmark designed to address the limitations of existing datasets like MixATIS and MixSNIPS by incorporating more complex and diverse utterances. Only utterances with two intents are used.

Metrics

Accuracy

Results

Adjusting Hyperparmeters (BERT)

Epochs
image/png
Weight decay
Weight decay 0.01 None
Accuracy 0.8744 0.8797
Learning rate
Learning rate 2e-5 5e-5 1e-4
Accuracy 0.8791 0.8797 0.8546
Batch size
Batch size 8 16
Accuracy 0.8797 0.8678

Selecting Models (BERT, RoBERTa, DeBERTa)

Models (Threshold=0.5)
Models BERT RoBERTa DeBERTa
Accuracy 0.8797 0.9055 0.8824
Threshold (=0.4)
Models BERT RoBERTa DeBERTa
Accuracy 0.8804 0.9055 0.8784

More Information

Base model: RoBERTa-base