# 🔑 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 as its base model and fine-tunes it on the OpenKP dataset. 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

# 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]

# Load pipeline
model_name = "ml6team/keyphrase-generation-t5-small-openkp"
generator = KeyphraseGenerationPipeline(model=model_name)

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 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.

## 👷‍♂️ 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( ; ).

from datasets import load_dataset
from transformers import AutoTokenizer
# Tokenizer
# 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,
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(
)
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
# 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.

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.