initial commit

1_Pooling/.json

@@ -0,0 +1,9 @@
+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false
+}

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

LICENSE

@@ -0,0 +1,201 @@
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NOTICE

@@ -0,0 +1,9 @@
+-------------------------------------------------------------------------------
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+
+-------------------------------------------------------------------------------
+Third party legal information
+
+

README.md

@@ -1,3 +1,197 @@
----
-license: apache-2.0
----
+
+<p align="center">
+  <img align="center" src="img/triple-encoder-logo_with_border.png" width="430px" />
+</p>
+
+<p align="center">
+    🤗 <a href="anonymous" target="_blank">Models</a> | 📊 <a href="anonymous" target="_blank">Datasets</a> | 📃 <a href="anonymous" target="_blank">Paper</a>
+</p>
+
+`triple-encoders` are models for contextualizing distributed [Sentence Transformers](https://sbert.net/) representations. This model was trained on the [DailyDialog](https://huggingface.co/datasets/daily_dialog) dataset and can be used for conversational sequence modeling and short-term planning via sequential modular late-interaction:
+<p align="center">
+  <img align="center" src="img/triple-encoder.jpg" width="1000px" />
+</p>
+
+Representations are encoded **separately** and the contextualization is **weightless**:
+1. *mean-pooling* to pairwise contextualize sentence representations (creates a distributed query)
+2. *cosine similarity* to measure the similarity between all query vectors and the retrieval candidates.
+3. *summation* to aggregate the similarity (similar to average-based late interaction of [ColBERT](https://github.com/stanford-futuredata/ColBERT)).
+
+## Key Features
+- 1️⃣ **One dense vector vs distributed dense vectors**: in our paper we demonstrate that our late interaction-based approach outperforms single-vector representations on long sequences, including zero-shot settings.
+- 🏎️💨 **Relative compute**: as every representation is encoded separately, you only need to encode, compute mixtures and similarities for the latest added representation (in dialog: the latest utterance). 
+- 📚 **No Limit on context-length**: our distributed sentence transformer architecture is not limited to any sequence length. You can use your entire sequence as query!
+- 🌎 **Multilingual support**: `triple-encoders` can be used with any [Sentence Transformers](https://sbert.net/) model. This means that you can model multilingual sequences by simply training on a multilingual model checkpoint. 
+
+
+## Installation
+You can install `triple-encoders` via pip:
+```bash
+pip install triple-encoders
+``` 
+Note that `triple-encoders` requires Python 3.6 or higher.
+
+# Getting Started
+
+Our experiments for sequence modeling and short-term planning conducted in the paper can be found in the `notebooks` folder. The hyperparameter that we used for training are the default parameters in the `trainer.py` file.
+
+## Retrieval-based Sequence Modeling
+We provide an example of how to use triple-encoders for conversational sequence modeling (response selection) with 2 dialog speakers. If you want to use triple-encoders for other sequence modeling tasks, you can use the `TripleEncodersForSequenceModeling` class.
+
+### Loading the model
+```python
+from triple_encoders.TripleEncodersForConversationalSequenceModeling import TripleEncodersForConversationalSequenceModeling
+
+triple_path = 'UKPLab/triple-encoders-dailydialog'
+
+# load model
+model = TripleEncodersForConversationalSequenceModeling(triple_path)
+```
+
+### Inference
+
+```python
+# load candidates for response selection 
+candidates = ['I am doing great too!','Where did you go?', 'ACL is an interesting conference']
+
+# load candidates and store index
+model.load_candidates_from_strings(candidates, output_directory_candidates_dump='output/path/to/save/candidates')
+
+# create a sequence
+sequence = model.contextualize_sequence(["Hi!",'Hey, how are you?'], k_last_rows=2)
+
+# model sequence (compute scores for candidates)
+sequence = model.sequence_modeling(sequence)
+
+# retrieve utterance from dialog partner
+new_utterance = "I'm fine, thanks. How are you?"
+
+# pass it to the model with dialog_partner=True 
+sequence = model.contextualize_utterance(new_utterance, sequence, dialog_partner=True)
+
+# model sequence (compute scores for candidates)
+sequence = model.sequence_modeling(sequence)
+
+# retrieve candidates to provide a response
+response = model.retrieve_candidates(sequence, 3)
+response
+#(['I am doing great too!','Where did you go?', 'ACL is an interesting conference'],
+# tensor([0.4944, 0.2392, 0.0483]))
+```
+**Speed:** 
+- Time to load candidates: 31.815 ms
+- Time to contextualize sequence: 18.078 ms
+- Time to model sequence: 0.256 ms
+- Time to contextualize new utterance: 15.858 ms
+- Time to model new utterance: 0.213 ms
+- Time to retrieve candidates: 0.093 ms
+
+### Evaluation
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("daily_dialog")
+test = dataset['test']['dialog']
+
+df = model.evaluate_seq_dataset(test, k_last_rows=2)
+df
+# pandas dataframe with the average rank for each history length
+```
+
+## Short-Term Planning (STP)
+Short-term planning enables you to re-rank candidate replies from LLMs to reach a goal utterance over multiple turns.
+
+### Inference
+
+```python
+from triple_encoders.TripleEncodersForSTP import TripleEncodersForSTP
+
+model = TripleEncodersForSTP(triple_path)
+
+context = ['Hey, how are you ?',
+           'I am good, how about you ?',
+           'I am good too.']
+
+candidates = ['Want to eat something out ?',
+              'Want to go for a walk ?']
+
+goal = ' I am hungry.'
+
+result = model.short_term_planning(candidates, goal, context)
+
+result
+# 'Want to eat something out ?'
+```
+### Evaluation
+
+```python
+from datasets import load_dataset
+from triple_encoders.TripleEncodersForSTP import TripleEncodersForSTP
+
+dataset = load_dataset("daily_dialog")
+test = dataset['test']['dialog']
+
+model = TripleEncodersForSTP(triple_path, llm_model_name_or_path='your favorite large language model')
+
+df = model.evaluate_stp_dataset(test)
+# pandas dataframe with the average rank and Hits@k for each history length, goal_distance
+```
+
+
+# Training Triple Encoders
+You can train your own triple encoders with Contextualized Curved Contrastive Learning (C3L) using our trainer. 
+The hyperparameters that we used for training are the default parameters in the `trainer.py` file. 
+Note that we pre-trained our best model with Curved Contrastive Learning (CCL) (from [imaginaryNLP](https://github.com/Justus-Jonas/imaginaryNLP)) before training with C3L.
+
+```python
+from triple_encoders.trainer import TripleEncoderTrainer
+from datasets import load_dataset
+
+dataset = load_dataset("daily_dialog")
+
+trainer = TripleEncoderTrainer(base_model_name_or_path=,
+                               batch_size=48,
+                               observation_window=5,
+                               speaker_token=True, # used for conversational sequence modeling
+                               num_epochs=3,
+                               warmup_steps=10000)
+
+trainer.generate_datasets(
+    dataset["train"]["dialog"],
+    dataset["validation"]["dialog"],
+    dataset["test"]["dialog"],
+)
+
+
+trainer.train("output/path/to/save/model")
+```
+## Citation
+If you use triple-encoders in your research, please cite the following paper:
+```
+% todo
+@article{anonymous,
+  title={Triple Encoders: Represenations That Fire Together, Wire Together},
+  author={Justus-Jonas Erker, Florian Mai, Nils Reimers, Gerasimos Spanakis, Iryna Gurevych},
+  journal={axiv},
+  year={2024}
+}
+```
+# Contact
+Contact person: Justus-Jonas Erker, justus-jonas.erker@tu-darmstadt.de
+
+https://www.ukp.tu-darmstadt.de/
+
+https://www.tu-darmstadt.de/
+
+Don't hesitate to send us an e-mail or report an issue, if something is broken (and it shouldn't be) or if you have further questions.
+This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication. 
+
+# License
+triple-encoders is licensed under the Apache License, Version 2.0. See [LICENSE](LICENSE) for the full license text.
+
+
+### Acknowledgement
+this package is based upon the [imaginaryNLP](https://github.com/Justus-Jonas/imaginaryNLP) and [Sentence Transformers](https://sbert.net/).
+
+
+

added_tokens.json

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+{
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+  "[B1]": 30526,
+  "[B2]": 30527,
+  "[BEFORE]": 30522,
+  "[E]": 30525,
+  "[O]": 30524
+}

config.json

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+{
+  "_name_or_path": "/netscratch/erker/models/Daily/gte_large_imaginaryEmbedding_UserToken_B42_5Fold_UPtoE15_thenlper_gte/15000/",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.31.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30528
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.31.0",
+    "pytorch": "1.10.0a0+0aef44c"
+  }
+}

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

pytorch_model.bin

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+oid sha256:8b2bf5d4fe2abf0c84367b205e5d22522f6f46f75aa399bf8a4a0f543b9e1766
+size 1340721011

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}