Model: munishair/keyword-tagging
Author: munishair
Tags: pytorch, safetensors, distilbert, keyphrase-extraction, en, dataset:midas/inspec, arxiv:2112.08547, license:mit, model-index, region:us
Card metadata: language: en
license: mit
tags: keyphrase-extraction
datasets: midas/inspec
metrics: seqeval
widget: text: Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document.  Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading  it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail  and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents,  this process can take a lot of time. 
Here is where Artificial Intelligence comes in. Currently, classical machine learning methods, that use statistical  and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture  the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency,  occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies  and context of words in a text.
example_title: Example 1, text: In this work, we explore how to learn task specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (up to 9.26 points in F1) over SOTA, when LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction. In the generative setting, we introduce a new pre-training setup for BART - KeyBART, that reproduces the keyphrases related to the input text in the CatSeq format, instead of the denoised original input. This also led to gains in performance (up to 4.33 points inF1@M) over SOTA for keyphrase generation. Additionally, we also fine-tune the pre-trained language models on named entity recognition(NER), question answering (QA), relation extraction (RE), abstractive summarization and achieve comparable performance with that of the SOTA, showing that learning rich representation of keyphrases is indeed beneficial for many other fundamental NLP tasks.
example_title: Example 2
model-index: name: DeDeckerThomas/keyphrase-extraction-distilbert-inspec
results: task: type: keyphrase-extraction
name: Keyphrase Extraction
dataset: type: midas/inspec
name: inspec
metrics: type: F1 (Seqeval)
value: 0.509
name: F1 (Seqeval)
verified: no, type: F1@M
value: 0.49
name: F1@M
verified: no
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Created: 2025-07-24 12:29:19