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color54/roberta-base-blendx2

sourceHugging Faceupdated 2y agoView on Hugging Face
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RoBERTa-base-BlendX2 model

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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)

1) Epochs image/png 2) Weight decay | Weight decay| 0.01 | None | |-----------------|----------|----------| | Accuracy | 0.8744 | 0.8797 | 3) Learning rate | Learning rate| 2e-5 | 5e-5 | 1e-4 | |------------------|----------|----------|----------| | Accuracy | 0.8791 | 0.8797 | 0.8546 | 4) Batch size | Batch size| 8 | 16 | |---------------|--------|--------| | Accuracy | 0.8797 | 0.8678 |

Selecting Models (BERT, RoBERTa, DeBERTa)

1) Models (Threshold=0.5) | Models | BERT | RoBERTa | DeBERTa | |--------------|-------------|-------------|-------------| | Accuracy | 0.8797 | 0.9055 | 0.8824 | 2) Threshold (=0.4) | Models | BERT | RoBERTa | DeBERTa | |--------------|-------------|-------------|-------------| | Accuracy | 0.8804 | 0.9055 | 0.8784 |

More Information

  • —Base model: RoBERTa-base