metadata

license: mit
datasets:
  - squad_v2
language:
  - en
widget:
  - text: >-
      when: Lionel Andrés Messi[note 1] (Spanish pronunciation: [ljoˈnel anˈdɾes
      ˈmesi] (listen); born 24 June 1987), also known as Leo Messi, is an
      Argentine professional footballer who plays as a forward for and captains
      both Major League Soccer club Inter Miami and the Argentina national team.
      Widely regarded as one of the greatest players of all time, Messi has won
      a record seven Ballon d'Or awards[note 2] and a record six European Golden
      Shoes, and in 2020 he was named to the Ballon d'Or Dream Team. Until
      leaving the club in 2021, he had spent his entire professional career with
      Barcelona, where he won a club-record 34
  - text: >-
      where: Lionel Andrés Messi[note 1] (Spanish pronunciation: [ljoˈnel
      anˈdɾes ˈmesi] (listen); born 24 June 1987), also known as Leo Messi, is
      an Argentine professional footballer who plays as a forward for and
      captains both Major League Soccer club Inter Miami and the Argentina
      national team. Widely regarded as one of the greatest players of all time,
      Messi has won a record seven Ballon d'Or awards[note 2] and a record six
      European Golden Shoes, and in 2020 he was named to the Ballon d'Or Dream
      Team. Until leaving the club in 2021, he had spent his entire professional
      career with Barcelona, where he won a club-record 34
  - text: >-
      how: Lionel Andrés Messi[note 1] (Spanish pronunciation: [ljoˈnel anˈdɾes
      ˈmesi] (listen); born 24 June 1987), also known as Leo Messi, is an
      Argentine professional footballer who plays as a forward for and captains
      both Major League Soccer club Inter Miami and the Argentina national team.
      Widely regarded as one of the greatest players of all time, Messi has won
      a record seven Ballon d'Or awards[note 2] and a record six European Golden
      Shoes, and in 2020 he was named to the Ballon d'Or Dream Team. Until
      leaving the club in 2021, he had spent his entire professional career with
      Barcelona, where he won a club-record 34
  - text: >-
      what: Lionel Andrés Messi[note 1] (Spanish pronunciation: [ljoˈnel anˈdɾes
      ˈmesi] (listen); born 24 June 1987), also known as Leo Messi, is an
      Argentine professional footballer who plays as a forward for and captains
      both Major League Soccer club Inter Miami and the Argentina national team.
      Widely regarded as one of the greatest players of all time, Messi has won
      a record seven Ballon d'Or awards[note 2] and a record six European Golden
      Shoes, and in 2020 he was named to the Ballon d'Or Dream Team. Until
      leaving the club in 2021, he had spent his entire professional career with
      Barcelona, where he won a club-record 34
  - text: >-
      where: Egypt (Egyptian Arabic: مصر Maṣr Egyptian Arabic pronunciation:
      [mɑsˤr]), officially the Arab Republic of Egypt, is a transcontinental
      country spanning the northeast corner of Africa and the Sinai Peninsula in
      the southwest corner of Asia. It is bordered by the Mediterranean Sea to
      the north, the Gaza Strip of Palestine and Israel to the northeast, the
      Red Sea to the east, Sudan to the south, and Libya to the west. The Gulf
      of Aqaba in the northeast separates Egypt from Jordan and Saudi Arabia.
      Cairo is the capital and largest city of Egypt, while Alexandria, the
      second-largest city, is an important industrial and tourist hub at the
      Mediterranean coast.[11] At approximately 100 million inhabitants, Egypt
      is the 14th-most populated country in the world, and the third-most
      populated in Africa, behind Nigeria and Ethiopia.
  - text: >-
      where: There is evidence of rock carvings along the Nile terraces and in
      desert oases. In the 10th millennium BCE, a culture of hunter-gatherers
      and fishers was replaced by a grain-grinding culture. Climate changes or
      overgrazing around 8000 BCE began to desiccate the pastoral lands of
      Egypt, forming the Sahara. Early tribal peoples migrated to the Nile River
      where they developed a settled agricultural economy and more centralized
      society.
  - text: >-
      when: By about 6000 BCE, a Neolithic culture took root in the Nile
      Valley.[31] During the Neolithic era, several predynastic cultures
      developed independently in Upper and Lower Egypt. The Badarian culture and
      the successor Naqada series are generally regarded as precursors to
      dynastic Egypt. The earliest known Lower Egyptian site, Merimda, predates
      the Badarian by about seven hundred years. Contemporaneous Lower Egyptian
      communities coexisted with their southern counterparts for more than two
      thousand years. The earliest known evidence of Egyptian hieroglyphic
      inscriptions appeared during the predynastic period on Naqada III pottery
      vessels, dated to about 3200 BCE.[32]
  - text: >-
      whose : or the next three millennia. Egyptian culture flourished during
      this long period and remained distinctively Egyptian in its religion,
      arts, language and customs. The first two ruling dynasties of a unified
      Egypt set the stage for the Old Kingdom period, c. 2700–2200 BCE, which
      constructed many pyramids, most notably the Third Dynasty pyramid of
      Djoser and the Fourth Dynasty Giza pyramids.
  - text: >-
      who:The First Intermediate Period ushered in a time of political upheaval
      for about 150 years.[33] Stronger Nile floods and stabilisation of
      government, however, brought back renewed prosperity for the country in
      the Middle Kingdom c. 2040 BCE, reaching a peak during the reign of
      Pharaoh Amenemhat III. A second period of disunity heralded the arrival of
      the first foreign ruling dynasty in Egypt, that of the Semitic Hyksos. The
      Hyksos invaders took over much of Lower Egypt around 1650 BCE and founded
      a new capital at Avaris. They were driven out by an Upper Egyptian force
      led by Ahmose I, who founded the Eighteenth Dynasty and relocated the
      capital from Memphis to Thebes.
library_name: transformers
tags:
  - generate answers
  - question generator
  - generate text
  - nlp
  - dataset maker
  - flan t5
  - t5

Model Card for QA_GeneraToR

drawing

TL;DR
Model Details
Usage
Uses
Bias, Risks, and Limitations
Training Details
Evaluation
Environmental Impact
Citation
Model Card Authors

my fine tuned model

This model is fine tuned to generate a question with answers from a context , why that can be very usful this can help you to generate a dataset from a book article any thing you would to make from it dataset and train another model on this dataset , give the model any context with pre prometed of quation you want + context and it will extarct question + answer for you this are promted i use [ "which", "how", "when", "where", "who", "whom", "whose", "why", "which", "who", "whom", "whose", "whereas", "can", "could", "may", "might", "will", "would", "shall", "should", "do", "does", "did", "is", "are", "am", "was", "were", "be", "being", "been", "have", "has", "had", "if", "is", "are", "am", "was", "were", "do", "does", "did", "can", "could", "will", "would", "shall", "should", "might", "may", "must", "may", "might", "must"]

TL;DR

If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract :

Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.

Model Details

Model Description

Model type: Language model
Language(s) (NLP): English
License: mit
Related Models: All FLAN-T5 Checkpoints
Original Checkpoints: All Original FLAN-T5 Checkpoints
Resources for more information:

Usage

Find below some example scripts on how to use the model in transformers:

Using the Pytorch model

Running the model on a CPU

Click to expand


from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("mohamedemam/QA_GeneraToR")
model = T5ForConditionalGeneration.from_pretrained("mohamedemam/QA_GeneraToR")

input_text = r"when: Lionel Andrés Messi[note 1] (Spanish pronunciation: [ljoˈnel anˈdɾes ˈmesi] (listen); born 24 June 1987), also known as Leo Messi, is an Argentine professional footballer who plays as a forward for and captains both Major League Soccer club Inter Miami and the Argentina national team. Widely regarded as one of the greatest players of all time, Messi has won a record seven Ballon d'Or awards[note 2] and a record six European Golden Shoes, and in 2020 he was named to the Ballon d'Or Dream Team. Until leaving the club"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Running the model on a GPU

Click to expand

# pip install accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("mohamedemam/QA_GeneraToR")
model = T5ForConditionalGeneration.from_pretrained("mohamedemam/QA_GeneraToR", device_map="auto")

input_text = r"when: Lionel Andrés Messi[note 1] (Spanish pronunciation: [ljoˈnel anˈdɾes ˈmesi] (listen); born 24 June 1987), also known as Leo Messi, is an Argentine professional footballer who plays as a forward for and captains both Major League Soccer club Inter Miami and the Argentina national team. Widely regarded as one of the greatest players of all time, Messi has won a record seven Ballon d'Or awards[note 2] and a record six European Golden Shoes, and in 2020 he was named to the Ballon d'Or Dream Team. Until leaving the club"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Running the model on a GPU using different precisions

FP16

Click to expand

# pip install accelerate
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("mohamedemam/QA_GeneraToR")
model = T5ForConditionalGeneration.from_pretrained("mohamedemam/QA_GeneraToR", device_map="auto", torch_dtype=torch.float16)

input_text = r"when: Lionel Andrés Messi[note 1] (Spanish pronunciation: [ljoˈnel anˈdɾes ˈmesi] (listen); born 24 June 1987), also known as Leo Messi, is an Argentine professional footballer who plays as a forward for and captains both Major League Soccer club Inter Miami and the Argentina national team. Widely regarded as one of the greatest players of all time, Messi has won a record seven Ballon d'Or awards[note 2] and a record six European Golden Shoes, and in 2020 he was named to the Ballon d'Or Dream Team. Until leaving the club"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

INT8

Click to expand

# pip install bitsandbytes accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("mohamedemam/QA_GeneraToR")
model = T5ForConditionalGeneration.from_pretrained("mohamedemam/QA_GeneraToR", device_map="auto", load_in_8bit=True)

input_text = r"when: Lionel Andrés Messi[note 1] (Spanish pronunciation: [ljoˈnel anˈdɾes ˈmesi] (listen); born 24 June 1987), also known as Leo Messi, is an Argentine professional footballer who plays as a forward for and captains both Major League Soccer club Inter Miami and the Argentina national team. Widely regarded as one of the greatest players of all time, Messi has won a record seven Ballon d'Or awards[note 2] and a record six European Golden Shoes, and in 2020 he was named to the Ballon d'Or Dream Team. Until leaving the club"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Uses

Direct Use and Downstream Use

The authors write in the original paper's model card that:

The primary use is research on language models, including: research on zero-shot NLP tasks and in-context few-shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models

See the research paper for further details.

Out-of-Scope Use

More information needed.

Bias, Risks, and Limitations

The information below in this section are copied from the model's official model card:

Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.

Ethical considerations and risks

Flan-T5 is fine-tuned on a large corpus of text data that was not filtered for explicit content or assessed for existing biases. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data.

Known Limitations

Flan-T5 has not been tested in real world applications.

Sensitive Use:

Flan-T5 should not be applied for any unacceptable use cases, e.g., generation of abusive speech.

Training Details

Training Data

The model was trained on a mixture of tasks, that includes the tasks described in the table below (from the original paper, figure 2):

Training Procedure

According to the model card from the original paper:

These models are based on pretrained T5 (Raffel et al., 2020) and fine-tuned with instructions for better zero-shot and few-shot performance. There is one fine-tuned Flan model per T5 model size.

The model has been trained on TPU v3 or TPU v4 pods, using t5x codebase together with jax.

Evaluation

Testing Data, Factors & Metrics

The authors evaluated the model on various tasks covering several languages (1836 in total). See the table below for some quantitative evaluation: For full details, please check the research paper.

Results

For full results for FLAN-T5-Large, see the research paper, Table 3.

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: Google Cloud TPU Pods - TPU v3 or TPU v4 | Number of chips ≥ 4.
Hours used: More information needed
Cloud Provider: GCP
Compute Region: More information needed
Carbon Emitted: More information needed

Citation

BibTeX:

@misc{https://doi.org/10.48550/arxiv.2210.11416,
  doi = {10.48550/ARXIV.2210.11416},
  
  url = {https://arxiv.org/abs/2210.11416},
  
  author = {Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret and Tay, Yi and Fedus, William and Li, Eric and Wang, Xuezhi and Dehghani, Mostafa and Brahma, Siddhartha and Webson, Albert and Gu, Shixiang Shane and Dai, Zhuyun and Suzgun, Mirac and Chen, Xinyun and Chowdhery, Aakanksha and Narang, Sharan and Mishra, Gaurav and Yu, Adams and Zhao, Vincent and Huang, Yanping and Dai, Andrew and Yu, Hongkun and Petrov, Slav and Chi, Ed H. and Dean, Jeff and Devlin, Jacob and Roberts, Adam and Zhou, Denny and Le, Quoc V. and Wei, Jason},
  
  keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Scaling Instruction-Finetuned Language Models},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution 4.0 International}
}

mohamedemam
/

Question_generator

Model Card for QA_GeneraToR

Table of Contents

my fine tuned model

TL;DR

Model Details

Model Description

Usage

Using the Pytorch model

Running the model on a CPU

Running the model on a GPU

Running the model on a GPU using different precisions

FP16

INT8

Uses

Direct Use and Downstream Use

Out-of-Scope Use

Bias, Risks, and Limitations

Ethical considerations and risks

Known Limitations

Sensitive Use:

Training Details

Training Data

Training Procedure

Evaluation

Testing Data, Factors & Metrics

Results

Environmental Impact

Citation