ConceptX

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Analyzing Latent Concept in Pre-trained Transformer Models

ConceptX 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# ConceptX
Analyzing Latent Concept in Pre-trained Transformer Models

The code has been split into three parts. 1) get concept clusters, 2) generate auto-labels data, 3) calculate the alignment between auto-labels and concepts.

## Get concept clusters
get_clusters/getclusters.sh provides step-by-step commands to create concept clusters. You need to specify path to a sentence file.

### Proprocessing
This step invovles tokenizing the input sentences and extracting word-level contextualized embeddings. The setup requires setting up neurox environment using env_neurox.yml.

```
conda env create --file=env_neuron.yml
```

### Run clustering
Cluster the word-level contextualized embeddings. This step requires setting up the clustering environment.

```
conda env create --file=env_clustering
```

## Generate Auto-labels
Label the sentence file with pre-defined concepts

### Linguistic Annotations
Label words with their linguistic information such as parts-of-speech, suffixes, wordNet, etc. The following command tags the sentence file with their part of speech information.

```
python --model_name "QCRI/bert-base-multilingual-cased-pos-english" --sentence_file data/text.in --output_file text.in.pos
```

### Trivial labels
auto-labels/Trivial/README provides step by step instructions to create trivial labels for the input sentence file.

## Calculate alignment
This step calculates the alignment score between a given label file and the concept clusters.

```
python scripts/align_with_single_auto_tag.py label_file sentence_file cluster_file
```

cluster_file is the cluster output of step 1. sentence_file is the list of sentences whose words are labeled.

ConceptX

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