README.md

metadata

language: en
license: mit
tags:
  - keyphrase-extraction
datasets:
  - midas/kptimes
metrics:
  - seqeval
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: >-
      FoodEx is the largest trade exhibition for food and drinks in Asia, with
      about 70,000 visitors checking out the products presented by hundreds of
      participating companies. I was lucky to enter as press; otherwise,
      visitors must be affiliated with the food industry— and pay ¥5,000 —  to
      enter. The FoodEx menu is global, including everything from cherry beer
      from Germany and premium Mexican tequila to top-class French and Chinese
      dumplings. The event was a rare chance to try out both well-known and
      exotic foods and even see professionals making them. In addition to booths
      offering traditional Japanese favorites such as udon and maguro sashimi,
      there were plenty of innovative twists, such as dorayaki , a sweet snack
      made of two pancakes and a red-bean filling, that came in coffee and
      tomato flavors. While I was there I was lucky to catch the World Sushi Cup
      Japan 2013, where top chefs from around the world were competing … and
      presenting a wide range of styles that you would not normally see in
      Japan, like the flower makizushi above.
    example_title: Example 2
model-index:
  - name: DeDeckerThomas/keyphrase-extraction-distilbert-kptimes
    results:
      - task:
          type: keyphrase-extraction
          name: Keyphrase Extraction
        dataset:
          type: midas/kptimes
          name: kptimes
        metrics:
          - type: seqeval
            value: 0.539
            name: F1-score

🔑 Keyphrase Extraction model: distilbert-kptimes

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 distilbert model on the kptimes dataset. More information can be found here: https://huggingface.co/distilbert-base-uncased.

The model is fine-tuned as a token classification problem where the text is labeled using the BIO scheme.

Label	Description
B-KEY	At the beginning of a keyphrase
I-KEY	Inside a keyphrase
O	Outside a keyphrase

✋ Intended uses & limitations

🛑 Limitations

• This keyphrase extraction model is very domain-specific and will perform very well on news articles from NY Times. It's not recommended to use this model for other domains, but you are free to test it out.
• Limited amount of predicted keyphrases.
• Only works for English documents.
• For a custom model, please consult the training notebook for more information (link incoming).

❓ How to use

from transformers import (
    TokenClassificationPipeline,
    AutoModelForTokenClassification,
    AutoTokenizer,
)
from transformers.pipelines import AggregationStrategy
import numpy as np

# Define keyphrase extraction pipeline
class KeyphraseExtractionPipeline(TokenClassificationPipeline):
    def __init__(self, model, *args, **kwargs):
        super().__init__(
            model=AutoModelForTokenClassification.from_pretrained(model),
            tokenizer=AutoTokenizer.from_pretrained(model),
            *args,
            **kwargs
        )

    def postprocess(self, model_outputs):
        results = super().postprocess(
            model_outputs=model_outputs,
            aggregation_strategy=AggregationStrategy.SIMPLE,
        )
        return np.unique([result.get("word").strip() for result in results])

# Load pipeline
model_name = "DeDeckerThomas/keyphrase-extraction-distilbert-kptimes"
extractor = KeyphraseExtractionPipeline(model=model_name)

# Inference
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 = extractor(text)

print(keyphrases)

# Output
['Artificial Intelligence' 'GANS' 'Keyphrase extraction'
 'classical machine learning' 'deep learning methods'
 'keyphrase extraction' 'linguistics' 'recurrent neural networks'
 'semantics' 'statistics' 'text analysis' 'transformers']

📚 Training Dataset

KPTimes is a keyphrase extraction/generation dataset consisting of 279,923 news articles from NY Times and 10K from JPTimes and annotated by professional indexers or editors.

You can find more information here: https://huggingface.co/datasets/midas/kptimes

👷‍♂️ Training procedure

For more in detail information, you can take a look at the training notebook (link incoming).

Training parameters

Parameter	Value
Learning Rate	1e-4
Epochs	50
Early Stopping Patience	3

Preprocessing

The documents in the dataset are already preprocessed into list of words with the corresponding labels. The only thing that must be done is tokenization and the realignment of the labels so that they correspond with the right subword tokens.

def preprocess_fuction(all_samples_per_split):
    tokenized_samples = tokenizer.batch_encode_plus(
        all_samples_per_split[dataset_document_column],
        padding="max_length",
        truncation=True,
        is_split_into_words=True,
        max_length=max_length,
    )
    total_adjusted_labels = []
    for k in range(0, len(tokenized_samples["input_ids"])):
        prev_wid = -1
        word_ids_list = tokenized_samples.word_ids(batch_index=k)
        existing_label_ids = all_samples_per_split[dataset_biotags_column][k]
        i = -1
        adjusted_label_ids = []

        for wid in word_ids_list:
            if wid is None:
                adjusted_label_ids.append(lbl2idx["O"])
            elif wid != prev_wid:
                i = i + 1
                adjusted_label_ids.append(lbl2idx[existing_label_ids[i]])
                prev_wid = wid
            else:
                adjusted_label_ids.append(
                    lbl2idx[
                        f"{'I' if existing_label_ids[i] == 'B' else existing_label_ids[i]}"
                    ]
                )

        total_adjusted_labels.append(adjusted_label_ids)
    tokenized_samples["labels"] = total_adjusted_labels
    return tokenized_samples

Postprocessing

For the post-processing, you will need to filter out the B and I labeled tokens and concat the consecutive B and Is. As last you strip the keyphrase to ensure all spaces are removed.

# Define post_process functions
def concat_tokens_by_tag(keyphrases):
    keyphrase_tokens = []
    for id, label in keyphrases:
        if label == "B":
            keyphrase_tokens.append([id])
        elif label == "I":
            if len(keyphrase_tokens) > 0:
                keyphrase_tokens[len(keyphrase_tokens) - 1].append(id)
    return keyphrase_tokens


def extract_keyphrases(example, predictions, tokenizer, index=0):
    keyphrases_list = [
        (id, idx2label[label])
        for id, label in zip(
            np.array(example["input_ids"]).squeeze().tolist(), predictions[index]
        )
        if idx2label[label] in ["B", "I"]
    ]

    processed_keyphrases = concat_tokens_by_tag(keyphrases_list)
    extracted_kps = tokenizer.batch_decode(
        processed_keyphrases,
        skip_special_tokens=True,
        clean_up_tokenization_spaces=True,
    )
    return np.unique([kp.strip() for kp in extracted_kps])

📝 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 KPTimes test set:

Dataset	P@5	R@5	F1@5	P@10	R@10	F1@10	P@M	R@M	F1@M
KPTimes Test Set	0.19	0.36	0.23	0.10	0.37	0.15	0.35	0.37	0.33

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.