adding examples

README.md

@@ -4,4 +4,46 @@ license: cc-by-nc-sa-4.0
 
 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
+This model does not use speaker tokens and was evaluated in the Long-Term planning and sequence modeling experiments.
+
+## 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"])
+seq.create_context(["Hi!",'Hey, how are you?'], precompute_top_p=0.8)
+
+# pre-compute and keep 80% of utterances
+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()
+```
+