README.md

---

language: en
license: mit
tags:
- keyphrase-extraction
datasets:
- midas/openkp
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 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: "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-openkp
  results:
  - task: 
      type: keyphrase-extraction
      name: Keyphrase Extraction
    dataset:
      type: midas/openkp
      name: openkp
    metrics:
      - type: seqeval 
        value: 0.430
        name: F1-score
---
# 🔑 Keyphrase Extraction model: distilbert-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 distilbert model on the OpenKP 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
* 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
```python
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])

```

```python
# Load pipeline
model_name = "ml6team/keyphrase-extraction-distilbert-openkp"
extractor = KeyphraseExtractionPipeline(model=model_name)
```

```python
# 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 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 = extractor(text)

print(keyphrases)
```

```
# Output
['keyphrase extraction', 'text analysis']
```

## 📚 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 | 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.
```python
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 Bs and Is. As last you strip the keyphrase to ensure all spaces are removed.
```python
# 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 OpenKP test set:

| Dataset           | P@5  | R@5  | F1@5 | P@10 | R@10 | F1@10 | P@M  | R@M  | F1@M |
|:-----------------:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|:----:|
| OpenKP Test Set   | 0.12 | 0.33 | 0.17 | 0.06 | 0.33 | 0.10  | 0.35 | 0.33 | 0.31 |

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.