---
language: en
license: mit
tags:
- keyphrase-generation
datasets:
- midas/openkp
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 NLP, transformers can be used to improve keyphrase extraction. Transformers 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-openkp
  results:
  - task: 
      type: keyphrase-generation
      name: Keyphrase Generation
    dataset:
      type: midas/openkp
      name: openkp
    metrics:
      - type: F1@M (Present)
        value: 0.246
        name: F1@M (Present)
      - type: F1@O (Present)
        value: 0.151
        name: F1@O (Present)
      - type: F1@M (Absent)
        value: 0.002
        name: F1@M (Absent)
      - type: F1@O (Absent)
        value: 7.56e-5
        name: F1@O (Absent)
---
# 🔑 Keyphrase Generation model: T5-small-OpenKP
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 NLP, transformers can be used to improve keyphrase extraction. Transformers 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](https://huggingface.co/t5-small) on the OpenKP dataset.

## ✋ Intended uses & limitations
### 🛑 Limitations
* Only works for English documents.
* For a custom model, please consult the training notebook for more information (link incoming).
* Sometimes the output doesn't make any sense.

### ❓ How to use
```python
# Model parameters
from transformers import (
    Text2TextGenerationPipeline,
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
)


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) if keyphrase != ""] for result in results]

```

```python
# Load pipeline
model_name = "ml6team/keyphrase-generation-t5-small-openkp"
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 NLP, 
transformers can be used to improve keyphrase extraction. Transformers 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']]
```

## 📚 Training Dataset
OpenKP is a large-scale, open-domain keyphrase extraction dataset with 148,124 real-world web documents along with 1-3 most relevant human-annotated keyphrases.

You can find more information here: https://github.com/microsoft/OpenKP.

## 👷‍♂️ 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
The documents in the dataset are already preprocessed into list of words with the corresponding keyphrases. The only thing that must be done is tokenization and joining all keyphrases into one string with a certain seperator of choice(;). 
```python
def pre_process_keyphrases(text_ids, kp_list):
    kp_order_list = []
    kp_set = set(kp_list)
    text = tokenizer.decode(
        text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
    )
    text = text.lower()
    for kp in kp_set:
        kp = kp.strip()
        kp_index = text.find(kp.lower())
        kp_order_list.append((kp_index, kp))
    kp_order_list.sort()
    present_kp, absent_kp = [], []
    for kp_index, kp in kp_order_list:
        if kp_index < 0:
            absent_kp.append(kp)
        else:
            present_kp.append(kp)
    return present_kp, absent_kp

def preprocess_fuction(samples):
    processed_samples = {"input_ids": [], "attention_mask": [], "labels": []}
    for i, sample in enumerate(samples[dataset_document_column]):
        input_text = " ".join(sample)
        inputs = tokenizer(
            input_text,
            padding="max_length",
            truncation=True,
        )
        present_kp, absent_kp = pre_process_keyphrases(
            text_ids=inputs["input_ids"],
            kp_list=samples["extractive_keyphrases"][i]
            + samples["abstractive_keyphrases"][i],
        )
        keyphrases = present_kp
        keyphrases += absent_kp
        target_text = f" {keyphrase_sep_token} ".join(keyphrases)
        with tokenizer.as_target_tokenizer():
            targets = tokenizer(
                target_text, max_length=40, padding="max_length", truncation=True
            )
            targets["input_ids"] = [
                (t if t != tokenizer.pad_token_id else -100)
                for t in targets["input_ids"]
            ]
        for key in inputs.keys():
            processed_samples[key].append(inputs[key])
        processed_samples["labels"].append(targets["input_ids"])
    return processed_samples
```
### Postprocessing
For the post-processing, you will need to split the string based on the keyphrase separator.
```python
def extract_keyphrases(examples):
    return [example.split(keyphrase_sep_token) for example in examples]
```

## 📝 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 OpenKP 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 |
|:-----------------:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|:----:|:----:|:----:|:----:|
| OpenKP Test Set   | 0.11 | 0.32 | 0.16 | 0.06 | 0.32 | 0.09  | 0.22 | 0.32 | 0.25 | 0.15 | 0.15 | 0.15 | 

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      |
|:-----------------:|:-----:|:-----:|:-----:|:------:|:-----:|:-------:|:-----:|:-----:|:-----:|:--------:|:--------:|:---------:|
| OpenKP Test Set   | 0.001 | 0.003 | 0.001 | 0.0004 | 0.004 | 0.0007  | 0.001 | 0.04  | 0.002 | 7.56e-e5 | 7.56e-e5 | 7.56e-e5  | 


For more information on the evaluation process, you can take a look at the keyphrase extraction evaluation notebook.

## 🚨 Issues
Please feel free to start discussions in the Community Tab.