transformers pytorch t5 text2text-generation keyphrase-generation en dataset:midas/openkp arxiv:1911.02671 license:mit model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- midas/openkp
language: en
license: mit
tags:
- keyphrase-generation
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-generation-t5-small-openkp
  results:
  - task:
      type: keyphrase-generation
      name: Keyphrase Generation
    dataset:
      name: openkp
      type: midas/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-05
      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 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.


## 📓 Model Description
This model uses [T5-small model](https://huggingface.co/t5-small) as its base model and fine-tunes it on the [OpenKP dataset](https://huggingface.co/datasets/midas/openkp). Keyphrase generation transformers are fine-tuned as a text-to-text generation problem where the keyphrases are generated. The result is a concatenated string with all keyphrases separated by a given delimiter (i.e. “;”). These models are capable of generating present and absent keyphrases.

## ✋ Intended Uses & Limitations
### 🛑 Limitations
* Only works for English documents.
* 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 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.
""".replace("\n", " ")

keyphrases = generator(text)

print(keyphrases)

```

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

## 📚 Training Dataset
[OpenKP](https://github.com/microsoft/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 in the [paper](https://arxiv.org/abs/1911.02671). 

## 👷‍♂️ Training Procedure
### 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
from datasets import load_dataset
from transformers import AutoTokenizer
# Tokenizer
tokenizer = AutoTokenizer.from_pretrained("t5-small", add_prefix_space=True)
# Dataset parameters
dataset_full_name = "midas/inspec"
dataset_subset = "raw"
dataset_document_column = "document"
keyphrase_sep_token = ";"
def preprocess_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 = preprocess_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
# Load dataset
dataset = load_dataset(dataset_full_name, dataset_subset)
# Preprocess dataset
tokenized_dataset = dataset.map(preprocess_fuction, batched=True)
    
```

### 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

Traditional evaluation methods are 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. In keyphrase generation you also look at F1@O where O stands for the number of ground truth 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  | 

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