README.md

---
language: en
license: mit
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- task-oriented-dialogues
- dialog-flow
datasets:
- sergioburdisso/dialog2flow-dataset
- Salesforce/dialogstudio
pipeline_tag: sentence-similarity
base_model:
- aws-ai/dse-bert-base
widget:
  - source_sentence: your phone please
    sentences:
      - please get their phone number
      - okay can i get your phone number please to make that booking
      - okay can i please get your id number
    output:
      - label: '0'
        score: 0.9
      - label: '1'
        score: 0.85
      - label: '2'
        score: 0.27
---

![image/png](voronoi_umap.png)

# **Dialog2Flow joint target model** (DSE-base)

This a variation of the **D2F$_{joint}$** model introduced in the paper ["Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction"](https://arxiv.org/abs/2410.18481) published in the EMNLP 2024 main conference.
This version uses DSE-base as the backbone model which yields to an increase in performance as compared to the vanilla version using BERT-base as the backbone (results reported in Appendix C).

Implementation-wise, this is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or search.

<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["your phone please", "okay may i have your telephone number please"]

model = SentenceTransformer('sergioburdisso/dialog2flow-joint-dse-base')
embeddings = model.encode(sentences)
print(embeddings)
```



## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['your phone please', 'okay may i have your telephone number please']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sergioburdisso/dialog2flow-joint-dse-base')
model = AutoModel.from_pretrained('sergioburdisso/dialog2flow-joint-dse-base')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```

## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 363506 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`spretrainer.losses.LabeledContrastiveLoss.LabeledContrastiveLoss` 

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 49478 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`spretrainer.losses.LabeledContrastiveLoss.LabeledContrastiveLoss` 

Parameters of the fit()-Method:
```
{
    "epochs": 15,
    "evaluation_steps": 164,
    "evaluator": [
        "spretrainer.evaluation.FewShotClassificationEvaluator.FewShotClassificationEvaluator"
    ],
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 3e-06
    },
    "scheduler": "WarmupLinear",
    "warmup_steps": 100,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 64, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citation


```bibtex
@inproceedings{burdisso-etal-2024-dialog2flow,
    title = "Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction",
    author = "Burdisso, Sergio  and
      Madikeri, Srikanth  and
      Motlicek, Petr",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami",
    publisher = "Association for Computational Linguistics",
}
```

## License

Copyright (c) 2024 [Idiap Research Institute](https://www.idiap.ch/).
MIT License.