Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (#2)

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	---
	language:
	- en
	tags:
	- summarization
	datasets:
	- xsum
	metrics:
	- rouge
	widget:
	- text: National Commercial Bank (NCB), Saudi Arabia’s largest lender by assets, agreed
	to buy rival Samba Financial Group for $15 billion in the biggest banking takeover
	this year.NCB will pay 28.45 riyals ($7.58) for each Samba share, according to
	a statement on Sunday, valuing it at about 55.7 billion riyals. NCB will offer
	0.739 new shares for each Samba share, at the lower end of the 0.736-0.787 ratio
	the banks set when they signed an initial framework agreement in June.The offer
	is a 3.5% premium to Samba’s Oct. 8 closing price of 27.50 riyals and about 24%
	higher than the level the shares traded at before the talks were made public.
	Bloomberg News first reported the merger discussions.The new bank will have total
	assets of more than $220 billion, creating the Gulf region’s third-largest lender.
	The entity’s $46 billion market capitalization nearly matches that of Qatar National
	Bank QPSC, which is still the Middle East’s biggest lender with about $268 billion
	of assets.
	model-index:
	- name: human-centered-summarization/financial-summarization-pegasus
	results:
	- task:
	type: summarization
	name: Summarization
	dataset:
	name: xsum
	type: xsum
	config: default
	split: test
	metrics:
	- type: rouge
	value: 35.2055
	name: ROUGE-1
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTA5OTZkY2YxMDU1YzE3NGJlMmE1OTg1NjlmNzcxOTg4YzY2OThlOTlkNGFhMGFjZWY4YjdiMjU5NDdmMWYzNSIsInZlcnNpb24iOjF9.ufBRoV2JoX4UlEfAUOYq7F3tZougwngdpKlnaC37tYXJU3omsR5hTsWM69hSdYO-k0cKUbAWCAMzjmoGwIaPAw
	- type: rouge
	value: 16.5689
	name: ROUGE-2
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWQwMmM2NjJjNzM1N2Y3NjZmMmE5NzNlNjRjNjEwNzNhNjcyZTRiMGRlODY3NWUyMGQ0YzZmMGFhODYzOTRmOSIsInZlcnNpb24iOjF9.AZZkbaYBZG6rw6-QHYjRlSl-p0gBT2EtJxwjIP7QYH5XIQjeoiQsTnDPIq25dSMDbmQLSZnpHC104ZctX0f_Dg
	- type: rouge
	value: 30.1285
	name: ROUGE-L
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTRjYThlMTllZjI4MGFiMDZhZTVkYmRjMTNhZDUzNTQ0OWQyNDQxMmQ5ODJiMmJiNGI3OTAzYjhiMzc2MTI4NCIsInZlcnNpb24iOjF9.zTHd3F4ZlgS-azl-ZVjOckcTrtrJmDOGWVaC3qQsvvn2UW9TnseNkmo7KBc3DJU7_NmlxWZArl1BdSetED0NCg
	- type: rouge
	value: 30.1706
	name: ROUGE-LSUM
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGMzZGFjNzVkYWI0NTJkMmZjZDQ0YjhiYjIxN2VkNmJjMTgwZTk1NjFlOGU2NjNjM2VjYTNlYTBhNTQ5MGZkNSIsInZlcnNpb24iOjF9.xQ2LoI3PwlEiXo1OT2o4Pq9o2thYCd9lSCKCWlLmZdxI5GxdsjcASBKmHKopzUcwCGBPR7zF95MHSAPyszOODA
	- type: loss
	value: 2.7092134952545166
	name: loss
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzQzODE0NDc5YTYzYjJlMWU2YTVjOGRjN2JmYWVkOWNkNTRlMTZlOWIyN2NiODJkMDljMjI3YzZmYzM3N2JjYSIsInZlcnNpb24iOjF9.Vv_pdeFuRMoKK3cPr5P6n7D6_18ChJX-2qcT0y4is3XX3mS98fk3U1AYEuy9nBHOwYR3o0U8WBgQ-Ya_FqefBg
	- type: gen_len
	value: 15.1414
	name: gen_len
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjk5OTk3NWRiNjZlZmQzMmYwOTU2MmQwOWE1MDNlNTg3YWVkOTgwOTc2ZTQ0MTBiZjliOWMyZTYwMDI2MDUzYiIsInZlcnNpb24iOjF9.Zvj84JzIhM50rWTQ2GrEeOU7HrS8KsILH-8ApTcSWSI6kVnucY0MyW2ODxvRAa_zHeCygFW6Q13TFGrT5kLNAA
	---

	### PEGASUS for Financial Summarization

	This model was fine-tuned on a novel financial news dataset, which consists of 2K articles from [Bloomberg](https://www.bloomberg.com/europe), on topics such as stock, markets, currencies, rate and cryptocurrencies.

	It is based on the [PEGASUS](https://huggingface.co/transformers/model_doc/pegasus.html) model and in particular PEGASUS fine-tuned on the Extreme Summarization (XSum) dataset: [google/pegasus-xsum model](https://huggingface.co/google/pegasus-xsum). PEGASUS was originally proposed by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu in [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/pdf/1912.08777.pdf).

	### How to use
	We provide a simple snippet of how to use this model for the task of financial summarization in PyTorch.

	```Python
	from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration

	# Let's load the model and the tokenizer
	model_name = "human-centered-summarization/financial-summarization-pegasus"
	tokenizer = PegasusTokenizer.from_pretrained(model_name)
	model = PegasusForConditionalGeneration.from_pretrained(model_name) # If you want to use the Tensorflow model
	# just replace with TFPegasusForConditionalGeneration


	# Some text to summarize here
	text_to_summarize = "National Commercial Bank (NCB), Saudi Arabia’s largest lender by assets, agreed to buy rival Samba Financial Group for $15 billion in the biggest banking takeover this year.NCB will pay 28.45 riyals ($7.58) for each Samba share, according to a statement on Sunday, valuing it at about 55.7 billion riyals. NCB will offer 0.739 new shares for each Samba share, at the lower end of the 0.736-0.787 ratio the banks set when they signed an initial framework agreement in June.The offer is a 3.5% premium to Samba’s Oct. 8 closing price of 27.50 riyals and about 24% higher than the level the shares traded at before the talks were made public. Bloomberg News first reported the merger discussions.The new bank will have total assets of more than $220 billion, creating the Gulf region’s third-largest lender. The entity’s $46 billion market capitalization nearly matches that of Qatar National Bank QPSC, which is still the Middle East’s biggest lender with about $268 billion of assets."

	# Tokenize our text
	# If you want to run the code in Tensorflow, please remember to return the particular tensors as simply as using return_tensors = 'tf'
	input_ids = tokenizer(text_to_summarize, return_tensors="pt").input_ids

	# Generate the output (Here, we use beam search but you can also use any other strategy you like)
	output = model.generate(
	input_ids,
	max_length=32,
	num_beams=5,
	early_stopping=True
	)

	# Finally, we can print the generated summary
	print(tokenizer.decode(output[0], skip_special_tokens=True))
	# Generated Output: Saudi bank to pay a 3.5% premium to Samba share price. Gulf region’s third-largest lender will have total assets of $220 billion
	```

	## Evaluation Results
	The results before and after the fine-tuning on our dataset are shown below:


	\| Fine-tuning \| R-1 \| R-2 \| R-L \| R-S \|
	\|:-----------:\|:-----:\|:-----:\|:------:\|:-----:\|
	\| Yes \| 23.55 \| 6.99 \| 18.14 \| 21.36 \|
	\| No \| 13.8 \| 2.4 \| 10.63 \| 12.03 \|


	## Citation

	You can find more details about this work in the following workshop paper. If you use our model in your research, please consider citing our paper:

	> T. Passali, A. Gidiotis, E. Chatzikyriakidis and G. Tsoumakas. 2021.
	> Towards Human-Centered Summarization: A Case Study on Financial News.
	> In Proceedings of the First Workshop on Bridging Human-Computer Interaction and Natural Language Processing(pp. 21–27). Association for Computational Linguistics.

	BibTeX entry:

	```
	@inproceedings{passali-etal-2021-towards,
	title = "Towards Human-Centered Summarization: A Case Study on Financial News",
	author = "Passali, Tatiana and Gidiotis, Alexios and Chatzikyriakidis, Efstathios and Tsoumakas, Grigorios",
	booktitle = "Proceedings of the First Workshop on Bridging Human{--}Computer Interaction and Natural Language Processing",
	month = apr,
	year = "2021",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/2021.hcinlp-1.4",
	pages = "21--27",
	}
	```

	## Support

	Contact us at [info@medoid.ai](mailto:info@medoid.ai) if you are interested in a more sophisticated version of the model, trained on more articles and adapted to your needs!

	More information about Medoid AI:
	- Website: [https://www.medoid.ai](https://www.medoid.ai)
	- LinkedIn: [https://www.linkedin.com/company/medoid-ai/](https://www.linkedin.com/company/medoid-ai/)