File size: 7,817 Bytes
e8044d4 0fa703d e8044d4 0fa703d e8044d4 0fa703d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
---
language: en
license: mit
tags:
- keyphrase-generation
datasets:
- midas/inspec
widget:
- text: "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a text. Since this is a time-consuming process, Artificial Intelligence is used to automate it. Currently, classical machine learning methods, that use statistics and linguistics, are widely used for the extraction process. The fact that these methods have been widely used in the community has the advantage that there are many easy-to-use libraries. Now with the recent innovations in deep learning methods (such as recurrent neural networks and transformers, GANS, …), keyphrase extraction can be improved. These new methods also focus on the semantics and context of a document, which is quite an improvement."
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-generation-t5-small-inspec
results:
- task:
type: keyphrase-generation
name: Keyphrase Generation
dataset:
type: midas/inspec
name: inspec
metrics:
- type: F1@M (P)
value: 0.000
name: F1@M (P)
- type: F1@O (P)
value: 0.000
name: F1@O (P)
- type: F1@M (A)
value: 0.000
name: F1@M (A)
- type: F1@O (A)
value: 0.000
name: F1@O (A)
---
# 🔑 Keyphrase Generation model: KeyBART-inspec
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a text. Since this is a time-consuming process, Artificial Intelligence is used to automate it.
Currently, classical machine learning methods, that use statistics and linguistics, are widely used for the extraction process. The fact that these methods have been widely used in the community has the advantage that there are many easy-to-use libraries.
Now with the recent innovations in deep learning methods (such as recurrent neural networks and transformers, GANS, …), keyphrase extraction can be improved. These new methods also focus on the semantics and context of a document, which is quite an improvement.
## 📓 Model Description
This model is a fine-tuned T5-small model on the Inspec dataset.
## ✋ Intended uses & limitations
### 🛑 Limitations
* This keyphrase generation model is very domain-specific and will perform very well on abstracts of scientific papers. It's not recommended to use this model for other domains, but you are free to test it out.
* Only works for English documents.
* For a custom model, please consult the training notebook for more information (link incoming).
* Sometimes the output can make no sense.
### ❓ How to use
```python
# Model parameters
from transformers import (
Text2TextGenerationPipeline,
AutoModelForSeq2SeqLM,
AutoTokenizer,
)
import numpy as np
class KeyphraseGenerationPipeline(Text2TextGenerationPipeline):
def __init__(self, model, keyphrase_sep_token=";", *args, **kwargs):
super().__init__(
model=AutoModelForSeq2SeqLM.from_pretrained(model),
tokenizer=AutoTokenizer.from_pretrained(model),
*args,
**kwargs
)
self.keyphrase_sep_token = keyphrase_sep_token
def postprocess(self, model_outputs):
results = super().postprocess(
model_outputs=model_outputs
)
return [[keyphrase.strip() for keyphrase in result.get("generated_text").split(self.keyphrase_sep_token)] for result in results]
```
```python
# Load pipeline
model_name = "DeDeckerThomas/keyphrase-generation-t5-small-inspec"
generator = KeyphraseGenerationPipeline(model=model_name)
```python
text = """
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a text.
Since this is a time-consuming process, Artificial Intelligence is used to automate it.
Currently, classical machine learning methods, that use statistics and linguistics, are widely used for the extraction process.
The fact that these methods have been widely used in the community has the advantage that there are many easy-to-use libraries.
Now with the recent innovations in deep learning methods (such as recurrent neural networks and transformers, GANS, …), keyphrase extraction can be improved.
These new methods also focus on the semantics and context of a document, which is quite an improvement.
""".replace(
"\n", ""
)
keyphrases = generator(text)
print(keyphrases)
```
```
# Output
[['keyphrase extraction', 'text analysis', 'artificial intelligence', 'classical machine learning', 'statistics']]
```
## 📚 Training Dataset
Inspec is a keyphrase extraction/generation dataset consisting of 2000 English scientific papers from the scientific domains of Computers and Control and Information Technology published between 1998 to 2002. The keyphrases are annotated by professional indexers or editors.
You can find more information here: https://huggingface.co/datasets/midas/inspec.
## 👷♂️ Training procedure
For more in detail information, you can take a look at the training notebook (link incoming).
### Training parameters
| Parameter | Value |
| --------- | ------|
| Learning Rate | 5e-5 |
| Epochs | 50 |
| Early Stopping Patience | 1 |
### Preprocessing
```python
```
### Postprocessing
```python
```
## 📝 Evaluation results
One of the traditional evaluation methods is the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases.
The model achieves the following results on the Inspec test set:
Extractive keyphrases
| Dataset | P@5 | R@5 | F1@5 | P@10 | R@10 | F1@10 | P@M | R@M | F1@M | P@O | R@O | F1@O |
|:-----------------:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|:----:|:----:|:----:|:----:|
| Inspec Test Set | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Abstractive keyphrases
| Dataset | P@5 | R@5 | F1@5 | P@10 | R@10 | F1@10 | P@M | R@M | F1@M | P@O | R@O | F1@O |
|:-----------------:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|:----:|:----:|:----:|:----:|
| Inspec Test Set | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
For more information on the evaluation process, you can take a look at the keyphrase extraction evaluation notebook.
## 🚨 Issues
Please feel free to contact Thomas De Decker for any problems with this model. |