language: en
license: mit
tags:
- keyphrase-generation
datasets:
- midas/inspec
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-inspec
results:
- task:
type: keyphrase-generation
name: Keyphrase Generation
dataset:
type: midas/inspec
name: inspec
metrics:
- type: F1@M (Present)
value: 0.317
name: F1@M (Present)
- type: F1@O (Present)
value: 0.279
name: F1@O (Present)
- type: F1@M (Absent)
value: 0.073
name: F1@M (Absent)
- type: F1@O (Absent)
value: 0.065
name: F1@O (Absent)
🔑 Keyphrase Generation model: T5-small-inspec
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 on the Inspec dataset.
✋ Intended uses & limitations
🛑 Limitations
- This keyphrase generation model is very domain-specific and will perform very well on abstracts of scientific papers. It's not recommended to use this model for other domains, but you are free to test it out.
- 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
# 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-inspec"
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', 'classical machine learning methods']]
📚 Training Dataset
Inspec is a keyphrase extraction/generation dataset consisting of 2000 English scientific papers from the scientific domains of Computers and Control and Information Technology published between 1998 to 2002. The keyphrases are annotated by professional indexers or editors.
You can find more information here: https://huggingface.co/datasets/midas/inspec.
👷♂️ 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(;).
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.
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 Inspec 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Inspec Test Set | 0.33 | 0.31 | 0.29 | 0.17 | 0.31 | 0.20 | 0.41 | 0.31 | 0.32 | 0.28 | 0.28 | 0.28 |
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Inspec Test Set | 0.05 | 0.09 | 0.06 | 0.03 | 0.09 | 0.04 | 0.08 | 0.09 | 0.07 | 0.06 | 0.06 | 0.06 |
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.