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README.md

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----
-license: mit
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+---
+license: mit
+---
+# UCTopic
+
+This repository contains the code of model UCTopic and an easy-to-use tool UCTopicTool used for <strong>Topic Mining</strong>, <strong>Unsupervised Aspect Extractioin</strong> or <strong>Phrase Retrieval</strong>.
+
+Our ACL 2022 paper [UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining](https://arxiv.org/abs/2202.13469).
+
+# Quick Links
+
+  - [Overview](#overview)
+  - [Pretrained Model](#pretrained-model)
+  - [Getting Started](#getting-started)
+    - [UCTopic Model](#uctopic-model)
+    - [UCTopicTool](#uctopictool)
+  - [Experiments in Paper](#experiments)
+    - [Requirements](#requirements)
+    - [Datasets](#datasets)
+    - [Entity Clustering](#entity-clustering)
+    - [Topic Mining](#topic-mining)
+  - [Pretraining](#pretraining)
+  - [Contact](#contact)
+  - [Citation](#citation)
+
+# Overview
+
+We propose UCTopic, a novel unsupervised contrastive learning framework for context-aware phrase representations and topic mining. UCTopic is pretrained in a large scale to distinguish if the contexts of two phrase mentions have the same semantics. The key to pretraining is positive pair construction from our phrase-oriented assumptions. However, we find traditional in-batch negatives cause performance decay when finetuning on a dataset with small topic numbers. Hence, we propose cluster-assisted contrastive learning(CCL) which largely reduces noisy negatives by selecting negatives from clusters and further improves phrase representations for topics accordingly.
+
+# Pretrained Model
+Our released model:
+|              Model              | Note|
+|:-------------------------------|------|
+|[uctopic-base](https://drive.google.com/file/d/1XQzi4E9ctdI373CK5O-pXQyBvOONssp1/view?usp=sharing)| Pretrained UCTopic model based on [LUKE-BASE](https://arxiv.org/abs/2010.01057)
+
+Unzip to get `uctopic-base` folder.
+
+# Getting Started
+We provide an easy-to-use phrase representation tool based on our UCTopic model. To use the tool, first install the uctopic package from PyPI
+```bash
+pip install uctopic
+```
+Or directly install it from our code
+```bash
+python setup.py install
+```
+
+## UCTopic Model
+After installing the package, you can load our model by just two lines of code
+```python
+from uctopic import UCTopic
+model = UCTopic.from_pretrained('JiachengLi/uctopic-base')
+```
+The model will automatically download pre-trained parameters from [HuggingFace's models](https://huggingface.co/models). If you encounter any problem when directly loading the models by HuggingFace's API, you can also download the models manually from the above table and use `model = UCTopic.from_pretrained({PATH TO THE DOWNLOAD MODEL})`.
+
+To get pre-trained <strong>phrase representations</strong>, our model inputs are same as [LUKE](https://huggingface.co/docs/transformers/model_doc/luke). Note: please input only <strong>ONE</strong> span each time, otherwise, will have performance decay according to our empirical results.
+
+```python
+from uctopic import UCTopicTokenizer, UCTopic
+
+tokenizer = UCTopicTokenizer.from_pretrained('JiachengLi/uctopic-base')
+model = UCTopic.from_pretrained('JiachengLi/uctopic-base')
+
+text = "Beyoncé lives in Los Angeles."
+entity_spans = [(17, 28)] # character-based entity span corresponding to "Los Angeles"
+
+inputs = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt")
+outputs, phrase_repr = model(**inputs)
+```
+`phrase_repr` is the phrase embedding (size `[768]`) of the phrase `Los Angeles`. `outputs` has the same format as the outputs from `LUKE`.
+
+## UCTopicTool
+We provide a tool `UCTopicTool` built on `UCTopic` for efficient phrase encoding, topic mining (or unsupervised aspect extraction) or phrase retrieval.
+
+### Initialization
+
+`UCTopicTool` is initialized by giving the `model_name_or_path` and `device`.
+```python
+from uctopic import UCTopicTool
+
+topic_tool = UCTopicTool('JiachengLi/uctopic-base', device='cuda:0')
+```
+
+### Phrase Encoding
+
+Phrases are encoded by our method `UCTopicTool.encode` in batches, which is more efficient than `UCTopic`.
+```python
+phrases = [["This place is so much bigger than others!", (0, 10)],
+           ["It was totally packed and loud.", (15, 21)],
+           ["Service was on the slower side.", (0, 7)],
+           ["I ordered 2 mojitos: 1 lime and 1 mango.", (12, 19)],
+           ["The ingredient weren't really fresh.", (4, 14)]]
+
+embeddings = topic_tool.encode(phrases) # len(embeddings) is equal to len(phrases)
+```
+**Note**: Each instance in `phrases` contains only one sentence and one span (character-level position) in format `[sentence, span]`.
+
+Arguments for `UCTopicTool.encode` are as follows,
+* **phrase** (List) - A list of `[sentence, span]` to be encoded.
+* **return_numpy** (bool, *optional*, defaults to `False`) - Return `numpy.array` or `torch.Tensor`.
+* **normalize_to_unit** (bool, *optional*, defaults to `True`) - Normalize all embeddings to unit vectors.
+* **keepdim** (bool, *optional*, defaults to `True`) - Keep dimension size `[instance_number, hidden_size]`.
+* **batch_size** (int, *optional*, defaults to `64`) - The size of mini-batch in the model.
+
+### Topic Mining and Unsupervised Aspect Extraction
+
+The method `UCTopicTool.topic_mining` can mine topical phrases or conduct aspect extraction from sentences with or without spans.
+
+```python
+sentences = ["This place is so much bigger than others!",
+             "It was totally packed and loud.",
+             "Service was on the slower side.",
+             "I ordered 2 mojitos: 1 lime and 1 mango.",
+             "The ingredient weren't really fresh."]
+
+spans = [[(0, 10)],                       # This place
+         [(15, 21), (26, 30)],            # packed; loud
+         [(0, 7)],                        # Service
+         [(12, 19), (21, 27), (32, 39)],  # mojitos; 1 lime; 1 mango
+         [(4, 14)]]                       # ingredient
+# len(sentences) is equal to len(spans)
+output_data, topic_phrase_dict = tool.topic_mining(sentences, spans, \
+                                                   n_clusters=[15, 25])
+
+# predict topic for new phrases
+phrases = [["The food here is amazing!", (4, 8)],
+           ["Lovely ambiance with live music!", (21, 31)]]
+
+topics = tool.predict_topic(phrases)
+```
+**Note**: If `spans` is not given, `UCTopicTool` will extract noun phrases by [spaCy](https://spacy.io/).
+
+Arguments for `UCTopicTool.topic_mining` are as follows,
+
+Data arguments:
+* **sentences** (List) - A List of sentences for topic mining.
+* **spans** (List, *optional*, defaults to `None`) - A list of span list corresponding sentences, e.g., `[[(0, 9), (5, 7)], [(1, 2)]]` and `len(sentences)==len(spans)`. If None, automatically mine phrases from noun chunks.
+
+Clustering arguments:
+* **n_clusters** (int or List, *optional*, defaults to `2`) - The number of topics. When `n_clusters` is a list, `n_clusters[0]` and `n_clusters[1]` will be the minimum and maximum numbers to search, `n_clusters[2]` is the search step length (if not provided, default to 1).
+* **meric** (str, *optional*, defaults to `"cosine"`) - The metric to measure the distance between vectors. `"cosine"` or `"euclidean"`.
+* **batch_size** (int, *optional*, defaults to `64`) - The size of mini-batch for phrase encoding.
+* **max_iter** (int, *optional*, defaults to `300`) - The maximum iteration number of kmeans.
+        
+CCL-finetune arguments:
+* **ccl_finetune** (bool, *optional*, defaults to `True`) - Whether to conduct CCL-finetuning in the paper.
+* **batch_size_finetune** (int, *optional*, defaults to `8`) - The size of mini-batch for finetuning.
+* **max_finetune_num** (int, *optional*, defaults to `100000`) - The maximum number of training instances for finetuning.
+* **finetune_step** (int, *optional*, defaults to `2000`) - The number of training steps for finetuning.
+* **contrastive_num** (int, *optional*, defaults to `5`) - The number of negatives in contrastive learning.
+* **positive_ratio** (float, *optional*, defaults to `0.1`) - The ratio of the most confident instances for finetuning.
+* **n_sampling** (int, *optional*, defaults to `10000`) - The number of sampled examples for cluster number confirmation and finetuning. Set to `-1` to use the whole dataset.
+* **n_workers** (int, *optional*, defaults to `8`) - The number of workers for preprocessing data.
+
+Returns for `UCTopicTool.topic_mining` are as follows,
+* **output_data** (List) - A list of sentences and corresponding phrases and topic numbers. Each element is `[sentence, [[start1, end1, topic1], [start2, end2, topic2]]]`.
+* **topic_phrase_dict** (Dict) - A dictionary of topics and the list of phrases under a topic. The phrases are sorted by their confidence scores. E.g., `{topic: [[phrase1, score1], [phrase2, score2]]}`.
+
+
+The method `UCTopicTool.predict_topic` predicts the topic ids for new phrases based on your training results from `UCTopicTool.topic_mining`. The inputs of `UCTopicTool.predict_topic` are same as `UCTopicTool.encode` and returns a list of topic ids (int).
+
+
+### Phrase Similarities and Retrieval
+
+The method `UCTopicTool.similarity` compute the cosine similarities between two groups of phrases:
+
+```python
+phrases_a = [["This place is so much bigger than others!", (0, 10)],
+           ["It was totally packed and loud.", (15, 21)]]
+
+phrases_b = [["Service was on the slower side.", (0, 7)],
+           ["I ordered 2 mojitos: 1 lime and 1 mango.", (12, 19)],
+           ["The ingredient weren't really fresh.", (4, 14)]]
+
+similarities = tool.similarity(phrases_a, phrases_b)
+```
+Arguments for `UCTopicTool.similarity` are as follows,
+* **queries** (List) - A list of `[sentence, span]` as queries.
+* **keys** (List or `numpy.array`) - A list of `[sentence, span]` as keys or phrase representations (`numpy.array`) from `UCTopicTool.encode`.
+* **batch_size** (int, *optional*, defaults to `64`) - The size of mini-batch in the model.
+
+`UCTopicTool.similarity` returns a `numpy.array` contains the similarities between phrase pairs in two groups.
+
+
+The methods `UCTopicTool.build_index` and `UCTopicTool.search` are used for phrase retrieval:
+```python
+phrases = [["This place is so much bigger than others!", (0, 10)],
+           ["It was totally packed and loud.", (15, 21)],
+           ["Service was on the slower side.", (0, 7)],
+           ["I ordered 2 mojitos: 1 lime and 1 mango.", (12, 19)],
+           ["The ingredient weren't really fresh.", (4, 14)]]
+
+# query multiple phrases
+query1 = [["The food here is amazing!", (4, 8)],
+           ["Lovely ambiance with live music!", (21, 31)]]  
+
+# query single phrases
+query2 = ["The food here is amazing!", (4, 8)]
+
+tool.build_index(phrases)
+results = tool.search(query1, top_k=3)
+# or
+results = tool.search(query2, top_k=3)
+```
+We also support [faiss](https://github.com/facebookresearch/faiss), an efficient similarity search library. Just install the package following [instructions](https://github.com/facebookresearch/faiss/blob/main/INSTALL.md) here and `UCTopicTool` will automatically use `faiss` for efficient search.
+
+`UCTopicTool.search` returns the ranked top k phrases for each query.
+
+
+### Save and Load finetuned UCTopicTool
+
+The methods `UCTopicTool.save` and `UCTopicTool.load` are used for save and load all paramters of `UCTopicTool`.
+
+Save:
+```python
+tool = UCTopicTool('JiachengLi/uctopic-base', 'cuda:0')
+# finetune UCTopic with CCL
+output_data, topic_phrase_dict = tool.topic_mining(sentences, spans, \
+                                                   n_clusters=[15, 25])
+
+tool.save(**your directory**)
+```
+
+Load:
+```python
+tool = UCTopicTool('JiachengLi/uctopic-base', 'cuda:0')
+tool.load(**your directory**)
+```
+The loaded parameters will be used for all methods (for encoding, topic mining, phrase similarities and retrieval) introduced above.
+
+# Experiments
+In this section, we re-implement experiments in our paper.
+
+## Requirements
+First, install PyTorch by following the instructions from [the official website](https://pytorch.org). To faithfully reproduce our results, please use the correct `1.9.0` version corresponding to your platforms/CUDA versions.
+
+Then run the following script to install the remaining dependencies,
+```bash
+pip install -r requirements.txt
+```
+
+Download `en_core_web_sm` model from spacy,
+```bash
+python -m spacy download en_core_web_sm
+```
+
+## Datasets
+The downstream datasets used in our experiments can be downloaded from [here](https://drive.google.com/file/d/1dVIp9li1Wdh0JgU8slsWm0ObcitbQtSL/view?usp=sharing).
+
+## Entity Clustering
+The config file of entity clustering is `clustering/consts.py` and most arguments are self-explained. Please setup `--gpu` and `--data_path` before running. The clustering scores will be printed.
+
+Clustering with our pre-trained phrase embeddings.
+```bash
+python clustering.py --gpu 0
+```
+Clustering with our pre-trained phrase embeddings and Cluster-Assisted Constrastive Learning (CCL) proposed in our paper.
+```bash
+python clustering_ccl_finetune.py --gpu 0
+```
+
+## Topic Mining
+The config file of entity clustering is `topic_modeling/consts.py`.
+
+**Key Argument Table**
+|    Arguments     | Description |
+|:-----------------|:-----------:|
+| --num_classes     |**Min** and **Max** number of classes, e.g., `[5, 15]`. Our model will find the class number by [silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html).|
+| --sample_num_cluster   |Number of sampled phrases to confirm class number.|
+| --sample_num_finetune|Number of sampled phrases for CCL finetuning.|
+| --contrastive_num|Number of negative classes for CCL finetuning.|
+| --finetune_step | CCL finetuning steps (maximum global steps for finetuning).|
+
+**Tips**: Please tune `--batch_size` or `--contrastive_num` for suitable GPU memory usage.
+
+Topic mining with our pre-trained phrase embeddings and Cluster-Assisted Constrastive Learning (CCL) proposed in our paper.
+```bash
+python find_topic.py --gpu 0
+```
+**Outputs**
+
+We output three files under `topic_results`:
+|    File Name     | Description |
+|:-----------------|:-----------:|
+| `merged_phraes_pred_prob.pickle` |A dictionary of phrases and their topic number and prediction probability. A topic of a phrase is merged from all phrase mentioins. `{phrase: [topic_id, probability]}`, e.g., {'fair prices': [0, 0.34889686]}|
+| `phrase_instances_pred.json`| A list of all mined phrase mentions. Each element is `[[doc_id, start, end, phrase_mention], topic_id]`.|
+| `topics_phrases.json`|A dictionary of topics and corresponding phrases sorted by probability. `{'topic_id': [[phrase1, prob1], [phrase2, prob2]]}`|
+
+### Pretraining
+
+**Data**
+
+For unsupervised pretraining of UCTopic, we use article and span with links from English Wikipedia and Wikidata. Our processed dataset can be downloaded from [here](https://drive.google.com/file/d/1wflsmhPI9J0ZA6aVRl2mQjHIE6JIvzAv/view?usp=sharing).
+
+**Training scripts**
+
+We provide example training scripts and our default training parameters for unsupervised training of UCTopic in `run_example.sh`.
+
+```bash
+bash run_example.sh
+```
+
+Arguments description can be found in `pretrain.py`. All the other arguments are standard Huggingface's `transformers` training arguments.
+
+**Convert models**
+
+Our pretrained checkpoints are slightly different from the checkpoint `uctopic-base`. Please refer `convert_uctopic_parameters.py` to convert it.
+
+# Contact
+
+If you have any questions related to the code or the paper, feel free to email Jiacheng (`j9li@eng.ucsd.edu`). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!
+
+# Citation
+
+Please cite our paper if you use UCTopic in your work:
+
+```bibtex
+@article{Li2022UCTopicUC,
+  title={UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining},
+  author={Jiacheng Li and Jingbo Shang and Julian McAuley},
+  journal={ArXiv},
+  year={2022},
+  volume={abs/2202.13469}
+}
+```