File size: 2,050 Bytes
954ef50 53a9268 9e59d5c 954ef50 ab7434f 616bc95 ab7434f 7d050cf 11beae9 7d050cf be6cb76 7d050cf 53a9268 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
---
license: cc-by-nc-sa-4.0
pipeline_tag: conversational
datasets:
- daily_dialog
---
⚠️ **This model is deprecated. Please don't use it as it produces embeddings of low quality.
We recommend using [triple-encoders](https://huggingface.co/UKPLab/triple-encoders-dailydialog) instead, also if you want to use them as a classic bi-encoder.**
Imaginary Embeddings utilize Curved Contrastive Learning (see paper [Imagination Is All You Need!](https://arxiv.org/pdf/2211.07591.pdf) (ACL 2023)) on [Sentence Transformers](https://sbert.net/) for long-short term dialogue planning and efficient abstract sequence modeling.
This model does not use speaker tokens and was evaluated in the Long-Term planning and sequence modeling experiments.
## setup
```bash
python -m pip install imaginaryNLP
```
## Usage Sequence Modeling:
```python
from imaginaryNLP.ImaginaryEmbeddingsForSequenceModeling import EvalImaginaryEmbeddingsForSequenceModeling
# Load the model
seq = EvalImaginaryEmbeddingsForSequenceModeling('Justus-Jonas/Imaginary-Embeddings-Classic', speaker_token=False)
# add candidates and context
seq.load_candidates_from_strings(["I'm fine, thanks. How are you?", "Where did you go?", "ACL is an interesting conference"])
# create context, pre-compute and keep 80% of utterances
seq.create_context(["Hi!",'Hey, how are you?'], precompute_top_p=0.8)
seq.sequence_modeling_with_precompute("I am doing good. Today I went for a walk. ")
```
## Long-Term-Planning
```python
from imaginaryNLP.ImaginaryEmbeddingsForLTP import ImaginaryEmbeddingsForLTP
ltp = ImaginaryEmbeddingsForLTP('Justus-Jonas/Imaginary-Embeddings-Classic', speaker_token=False)
# add a contex
ltp.create_context([' Hello', 'Hi , great to meet you ! '])
# add goals
ltp.add_goal(" great to hear that ! ")
ltp.add_goal(" Want to go for a walk ? ")
ltp.add_goal(" Bye !")
# greedy curving
ltp.greedy_curving()
# imaginary embedding chains
ltp.imaginary_embedding_chains()
# imaginary embedding chains with curving
ltp.imaginary_embedding_chains_with_curving()
``` |