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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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11.7k
| library_name
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GKLMIP/bert-khmer-base-uncased-tokenized
|
GKLMIP
| 2021-07-31T03:07:47Z | 55 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
https://github.com/GKLMIP/Pretrained-Models-For-Khmer
If you use our model, please consider citing our paper:
```
@article{,
author="Jiang, Shengyi
and Fu, Sihui
and Lin, Nankai
and Fu, Yingwen",
title="Pre-trained Models and Evaluation Data for the Khmer Language",
year="2021",
publisher="Tsinghua Science and Technology",
}
```
|
GKLMIP/bert-khmer-base-uncased
|
GKLMIP
| 2021-07-31T03:07:24Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
https://github.com/GKLMIP/Pretrained-Models-For-Khmer
If you use our model, please consider citing our paper:
```
@article{,
author="Jiang, Shengyi
and Fu, Sihui
and Lin, Nankai
and Fu, Yingwen",
title="Pre-trained Models and Evaluation Data for the Khmer Language",
year="2021",
publisher="Tsinghua Science and Technology",
}
```
|
GKLMIP/roberta-tagalog-base
|
GKLMIP
| 2021-07-31T02:43:47Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
https://github.com/GKLMIP/Pretrained-Models-For-Tagalog
If you use our model, please consider citing our paper:
```
@InProceedings{,
author="Jiang, Shengyi
and Fu, Yingwen
and Lin, Xiaotian
and Lin, Nankai",
title="Pre-trained Language models for Tagalog with Multi-source data",
booktitle="Natural Language Processing and Chinese Computing",
year="2021",
publisher="Springer International Publishing",
address="Cham",
}
```
|
veronica320/TE-for-Event-Extraction
|
veronica320
| 2021-07-30T23:11:05Z | 127 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
# TE-for-Event-Extraction
## Model description
This is a TE model as part of the event extraction system in the ACL2021 paper: [Zero-shot Event Extraction via Transfer Learning: Challenges and Insights](https://aclanthology.org/2021.acl-short.42/). The pretrained architecture is [roberta-large](https://huggingface.co/roberta-large) and the fine-tuning data is [MNLI](https://cims.nyu.edu/~sbowman/multinli/).
The label mapping is:
```
LABEL_0: Contradiction
LABEL_1: Neutral
LABEL_2: Entailment
```
## Demo
To see how the model works, type a sentence and a hypothesis separated by "\<\/s\>\<\/s\>" in the right-hand-side textbox under "Hosted inference API".
Example:
- Input:
```
A car bomb exploded Thursday in a crowded outdoor market in the heart of Jerusalem. </s></s> This text is about an attack.
```
- Output:
```
LABEL_2 (Entailment)
```
## Usage
- To use the TE model independently, follow the [huggingface documentation on AutoModelForSequenceClassification](https://huggingface.co/transformers/task_summary.html#sequence-classification).
- To use it as part of the event extraction system, please check out [our Github repo](https://github.com/veronica320/Zeroshot-Event-Extraction).
### BibTeX entry and citation info
```
@inproceedings{lyu-etal-2021-zero,
title = "Zero-shot Event Extraction via Transfer Learning: {C}hallenges and Insights",
author = "Lyu, Qing and
Zhang, Hongming and
Sulem, Elior and
Roth, Dan",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.42",
doi = "10.18653/v1/2021.acl-short.42",
pages = "322--332",
abstract = "Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies. In this work, we explore the possibility of zero-shot event extraction by formulating it as a set of Textual Entailment (TE) and/or Question Answering (QA) queries (e.g. {``}A city was attacked{''} entails {``}There is an attack{''}), exploiting pretrained TE/QA models for direct transfer. On ACE-2005 and ERE, our system achieves acceptable results, yet there is still a large gap from supervised approaches, showing that current QA and TE technologies fail in transferring to a different domain. To investigate the reasons behind the gap, we analyze the remaining key challenges, their respective impact, and possible improvement directions.",
}
```
|
huggingtweets/artstarcross
|
huggingtweets
| 2021-07-30T15:33:38Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/artstarcross/1627659166884/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1406785434093604864/pNV1y7vJ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Starcross</div>
<div style="text-align: center; font-size: 14px;">@artstarcross</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Starcross.
| Data | Starcross |
| --- | --- |
| Tweets downloaded | 1846 |
| Retweets | 217 |
| Short tweets | 67 |
| Tweets kept | 1562 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/177l3jal/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @artstarcross's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2w1qo4hm) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2w1qo4hm/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/artstarcross')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
valhalla/t5-small-e2e-qg
|
valhalla
| 2021-07-30T13:10:33Z | 1,228 | 7 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question-generation",
"dataset:squad",
"arxiv:1910.10683",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
datasets:
- squad
tags:
- question-generation
widget:
- text: "Python is developed by Guido Van Rossum and released in 1991. </s>"
license: mit
---
## T5 for question-generation
This is [t5-small](https://arxiv.org/abs/1910.10683) model trained for end-to-end question generation task. Simply input the text and the model will generate multile questions.
You can play with the model using the inference API, just put the text and see the results!
For more deatils see [this](https://github.com/patil-suraj/question_generation) repo.
### Model in action 🚀
You'll need to clone the [repo](https://github.com/patil-suraj/question_generation).
[](https://colab.research.google.com/github/patil-suraj/question_generation/blob/master/question_generation.ipynb)
```python3
from pipelines import pipeline
text = "Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum \
and first released in 1991, Python's design philosophy emphasizes code \
readability with its notable use of significant whitespace."
nlp = pipeline("e2e-qg")
nlp(text)
=> [
'Who created Python?',
'When was Python first released?',
"What is Python's design philosophy?"
]
```
|
abhishek/autonlp-ferd1-2652021
|
abhishek
| 2021-07-30T12:27:14Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-ferd1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-ferd1
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2652021
## Validation Metrics
- Loss: 0.3934604227542877
- Accuracy: 0.8411030860144452
- Precision: 0.8201550387596899
- Recall: 0.8076335877862595
- AUC: 0.8946767157983608
- F1: 0.8138461538461538
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-ferd1-2652021
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-ferd1-2652021", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-ferd1-2652021", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
m3hrdadfi/gpt2-persian-qa
|
m3hrdadfi
| 2021-07-30T09:00:42Z | 55 | 6 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"fa",
"dataset:persian_qa",
"dataset:parsinlu_reading_comprehension",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: fa
datasets:
- persian_qa
- parsinlu_reading_comprehension
tags:
- text-generation
widget:
- text: "قرارداد کرسنت قراردادی برای فروش روزانه معادل 500 میلیون فوت مکعب، گاز ترش میدان سلمان است، که در سال 1381 و در زمان وزارت بیژن نامدار زنگنه در دولت هفتم مابین شرکت کرسنت پترولیوم و شرکت ملی نفت ایران منعقد گردید. مذاکرات اولیه این قرارداد از سال 1997 آغاز شد و در نهایت، سال 2001 ( 1381 ) به امضای این تفاهم نامه مشترک انجامید. بر اساس مفاد این قرارداد، مقرر شده بود که از سال 2005 با احداث خط لوله در خلیج فارس، گاز فرآورده نشده میدان سلمان (مخزن مشترک با ابوظبی)، به میزان روزانه 500 میلیون فوت مکعب (به قول برخی منابع 600 میلیون فوت مکعب) به امارات صادر شود. این قرارداد مطابق قوانین داخلی ایران بسته شدهو تنها قرارداد نفتی ایران است که از طرف مقابل خود، تضمین گرفتهاست. اجرای این پروژه در سال 1384 با دلایل ارایه شده از سوی دیوان محاسبات ایران از جمله تغییر نیافتن بهای گاز صادراتی و ثابت ماندن آن در هفت سال اول اجرای قرارداد متوقف شد. این در حالی است که طبق تعریف حقوقی، دیوان محاسبات ایران، حق دخالت در قراردادها، پیش از آنکه قراردادها اجرایی و مالی شوند را ندارد. پرسش: طرفین قرار داد کرسنت کیا بودن؟ پاسخ:"
- text: "ناف جایی قرار گرفته که در واقع بندناف در داخل رحم در آنجا به شکم جنین وصل بودهاست. بندناف که جفت را به جنین متصل کرده بعد از تولد از نوزاد جدا میشود. برای جدا کردن بند ناف از دو پنس استفاده میکنند و بین آن دو را میبرند. پنس دیگری نزدیک شکم نوزاد قرار داده میشود که بعد از دو روز برداشته خواهد شد. بندناف باقیمانده طی 15 روز خشک شده و میافتد و به جای آن اسکاری طبیعی به جای میماند. البته بر خلاف تصور عامه مردم شکل ناف در اثر بریدن بند ناف به وجود نمیآید و پیش از این در شکم مادر حالت ناف شکل گرفتهاست. شکل ناف در میان مردم مختلف متفاوت است و اندازه آن بین 1 ٫ 5 تا 2 سانتیمتر است. تمام پستانداران جفتزیست ناف دارند. ناف در انسانها به سادگی قابل مشاهدهاست. پرسش: بند ناف انسان به کجا وصل است؟ پاسخ:"
- text: "بیش از ده هزار سال است که انسانها در قاره آمریکا زندگی میکنند. قاره آمریکا توسط کریستف کلمب و در سال 1492 کشف شد اما او به اشتباه فکر کرد که آنجا هندوستان است اما مدتها بعد آمریگو وسپوچی اعلام کرد که این قاره جدیدی است. اما تاریخ آمریکا به عنوان یک کشور مستقل به سال 1783 میلادی بازمیگردد که در آن آمریکا بر طبق معاهده پاریس به رسمیت شناخته گردید. پرسش: قاره آمریکا در چه سالی کشف شد؟ پاسخ:"
- text: "الکترونیک آرتز یا بهطور مختصر ایای شرکتی آمریکایی است که از بزرگترین شرکتهای تولید و توزیع بازیهای رایانهای بهشمار میآید. تریپ هاوکینگز این شرکت را در سال 1982 ت سیس کرد و هدف اولیه او تولید انواعی از بازیهای رایانهای بود که در خانه میتوان با آنها بازی کرد. ایای در اواخر دهه 80 به بهبود و توسعه حوزه کاری خود در زمینه بازیهای رایانهای پرداخت و با جذب چندین چهره مبتکر، موفق به رشد و توسعه بسیار در این زمینه شد. شرکت ایای در سال 2007 رتبه هشتم در فهرست بزرگترین شرکتهای طراحی نرمافزار را به خود اختصاص داد. درآمد سالانه شرکت ایای در مه 2008 به بیش از 4 ٫ 02 میلیارد دلار رسید و این مقدار، رو به افزایش است. موفقترین بازیهای ایای، بازیهای ورزشی (که توسط بخش ایای اسپورتز، وابسته به این شرکت تولید میشود)، بازیهای برگرفته از فیلمهای محبوب و البته بازیهای معروفی است که این شرکت همواره به ساختن آنها مشغول بودهاست از جمله این بازیها میتوان به بازیهایی مانند نید فور اسپید، مدال افتخار، سیمز، بتل فیلد و برن اوت اشاره کرد. یک نکته حایز اهمیت در مورد این شرکت این است که در جمع 5 شرکت منفور دنیا قرار دارد. پرسش: بازیهای سبک ورزشی شرکت الکترونیک آرتز توسط کدوم قسمت ساخته میشه؟ پاسخ:"
- text: "کویر یا نمک زار منطقهای است که به دلیل موقعیت جغرافیایی (معمولا ختم رودخانهها در آن) و حرارت شدید آفتاب به نمکزار بدل شده باشد. برخی کویرها قبلا دریاچه یا دریاهایی بودهاند که در اثر تبخیر آب از آنها به نمکزار بدل شدهاند. کویر مرکزی ایران که دشت کویر نامیده میشود، درون خود تعداد زیادی کویر کوچکتر، مانند کویر درانجیر، کویر ساغند، کویر بند ریگ را جا دادهاست. با وجود اینکه در بین عامه مردم رایج است که اصطلاح 'کویر' و 'بیابان' را بهجای یکدیگر بهکار میبرند ولی بین این دو اصطلاح تفاوت اساسی وجود دارد. بیابان به بخشی از مناطق خشک گفته میشود که بارندگی سالانه آن کمتر از 50 میلیمتر است و ممکن است چند سال در آن باران نبارد و با کمآبی و تبخیر شدید مواجه است و پوشش گیاهی آن بسیار ضعیف است. اما کویر به زمینهای رسی پفکرده، با شوری و نمک بسیار شدید گفته میشود که گیاهان نمیتوانند در آن رشد نمایند. در بعضی از کویرها که شوری خاک کمتر است، ممکن است گیاهانی مانند گز که دربرابر املاح نمکی مقاوم است، در آن رشد نماید. پرسش: بافت گیاهی در کویر چگونه است؟ پاسخ:"
- text: "قطبنما وسیلهای برای تعیین جهت (جهتیابی) است. این وسیله با استفاده از میدان مغناطیسی زمین جهت قطب شمال را نشان میدهد که در حقیقت شمال مغناطیسی زمین است که با شمال حقیقی مقداری فاصله دارد. زاویه بین شمال حقیقی و شمال مغناطیسی، میل مغناطیسی نامیده میشود. امروزه برای تعیین شمال حقیقی از قطبنماهای پیشرفتهتری مانند قطبنمای ژیروسکوپی استفاده میشود. قطبنمایی که از یک آهنربا ساخته شده یعنی قطبنمای مغناطیسی جهت را نشان میدهد زیرا زمین چون آهنربای بزرگی عمل میکند. نیروی آهنربایی زمین قطبنما یا سوزن مغناطیسی را به سوی شمال و جنوب میکشد. کسی نمیداند که چه کسی اول بار قطبنما را ساخت. برخی گمان میکنند که چینیان نخستین بار قطبنما را ساختند برخی دیگر میگویند که قطبنما در ایتالیا اختراع شدهاست. بعضی از نخستین قطبنماها تکههای اکسید مغناطیسی آهن بودهاند که بر قطعات چوبی یا چوبپنبه قرار داشتند و در یک ظرف آب شناور بودند. اکسید مغناطیسی آهن نوعی کانی آهن است یک نام دیگر آن ماگنتیت است. تکههای ماگنتیت آهنرباهای طبیعی هستند. پس از آن مردم ساختن آهنربا از فولاد را یادگرفتند و توانستند قطبنماهای بهتری بسازند. پرسش: اکسید مغناطیسی آهن چیه؟ پاسخ:"
- text: "لاستیک طبیعی که لاستیک هندی یا کایوچو نیز نامیده میشود، قدیمیترین الاستومر تجاری است که از لاتکس ساخته میشود. لاتکس ترشحات داخلی یک درخت گرمسیری به نام درخت لاستیک است. لاتکس در شکل خام خود، نوعی چسب بسیار خوب است و میتوان با انحلال آن در حلالهای مناسب، چسبهای مختلفی تولید کرد. لاتکس در ابتدای تولید، از پلیمرهایی از ترکیب آلی ایزوپرین با ناخالصیهای جزیی از سایر ترکیبات آلی، به علاوه آب تشکل شدهاست. تایلند، مالزی و اندونزی کشورهای پیشرو در تولید لاستیک هستند. انواع پلی ایزوپرین که به عنوان لاستیکهای طبیعی استفاده میشوند، در دسته الاستومرها طبقهبندی میشوند. اولین استفاده از لاستیک توسط فرهنگهای بومی آمریکای میانه انجام شد. آنها از این لاستیک برای ساخت توپ بازی استفاده میکردند. بعدها لاستیک توسط فرهنگهای مایا و آزتک مورد استفاده قرار گرفت. آزتکها علاوه بر ساخت توپ، از لاستیک برای اهداف دیگری مانند ساخت ظروف و ضدآب ساختن منسوجات از طریق اشباع آنها با شیره لاتکس استفاده میکردند. پرسش: آمریکای میانه در ابتدا از لاستیک برای تولید چی استفاده میکرد؟ پاسخ:"
- text: "آتیلا ( 405 453 میلادی) یکی از رهبران قوم هون بود که بزرگترین امپراتوری را در اروپا، از رود اورال تا دانوب تشکیل داد. در زمان فرمانروایی، وی یکی از مخوفترین دشمنان امپراتوریهای روم غربی و شرقی بود. رومیان به او لقب تازیانه خداوند داده بودندو به او باج میدادند تا کاری به کار رم نداشته باشد. آتیلا در آغاز به ایران حمله کرد و با شکست مواجه شد. حملهای که او در سال 441 میلادی به امپراتوری بیزانس کرد باعث شد تا تصمیم به حملات بیشتری به سوی غرب بگیرد. وی در اروپا شهرهای بسیاری را نابود و غارت کرد.سرانجام، در نبرد دشت کاتالانیها، در مقابل فلاویوس آییتیوس شکست خورد. در این جنگ، رومیها و آلانیها به مصاف با هونها رفتند.هونها در ناحیه بین رود ولگا و دشتهای مجارستان میزیستند، از آغاز سده پنجم به تاخت و تازهای فراوان و پرسودی در حوالی رود دانوب دست زدند، بنابراین، در حدود 445 تا 440 میلادی، دربار آتیلا به تجمل و زیبایی آراسته بود، شماره اسیرانی که میگرفتند بسیار بود، هر دو زبان یونانی و لاتین در دربار تکلم میشد، و دبیران رومیتبار رویدادهای خارجی را همواره به آگاهی خان میرساندند، آتیلا، زرد رنگتر از بیشتر افراد قومش بود، پرسش: رومیها چه لقبی به اتیلا داده بودند؟ پاسخ:"
- text: "ماده سوختنی مادهای است که در اثر تغییرات (معمولا شیمیایی) تولید انرژی مفید میکند که بعدا میتواند تبدیل به انرژی مکانیکی شود. این تغییرات معمولا با سوختن (یعنی ترکیب با اکسیژن) همراه است. فرایندهای مورد استفاده برای تبدیل سوخت به انرژی عبارتند از: واکنشهای شیمیایی مختلف و گرمازا، واکنشهای هستهای مانند شکافت هستهای یا گداخت هستهای. هیدروکربنها تا حد زیادی شایعترین منبع سوخت مورد استفاده توسط انسان است، اما در بسیاری از موارد فلزات رادیو اکتیو نیز استفاده میشوند. اولین استفاده از سوخت توسط بشر ، احتراق و سوزاندن تکههای چوب در حدود 2 میلیون سال پیش توسط انسان راست قامت بود . به صورت کلی در طول تاریخ زندگی بشر که تا به حال با آن آشنا شدهایم ، تنها سوخت هایی که بیشترین استفاده را داشته است از گیاهان و یا چربی حیوانات بدست میآمده است و مورد استفاده انسان قرار گرفته است . انسانها از 6000 سال قبل از میلاد مسیح برای ذوب آهن از زغال چوب و مشتقات چوب استفاده میکردند. بعدها این سوختها جای خودشان را با کک عوض کردند . به دلیل اینکه در حوالی قرن 18 جنگلهای اروپا در حال نابودی بودند. پرسش: سوخت چجوری انرژی قابل استفاده تولید میکنه؟ پاسخ:"
- text: "ژرمن شپرد یا سگ چوپان آلمانی یکی از نژادهای سگ است. سگ چوپان آلمانی یکی از نژادهای اصیل آلمانی است که برای نخستین بار در سال 1899 ثبت گردید. سگی باهوش، شجاع و مناسب برای کارهای مختلف از جمله گله داری، نگهبانی، راهنمای نابینایان، همراه خانواده، و جستجو و نجات است. قد استاندارد تا جدوگاه در نرها 60 تا 65 سانتیمتر و در مادهها 55 تا 60 سانتیمتر است. طول عمر از 9 تا 13 سال است. این نژاد را اکثر افراد به دلیل استفاده در فیلمهایی نظیر رکس میشناسند و همچنین این سگ حضور موثری در صحنههای امدادی دارد. در خاورمیانه دستههایی از شپردهای پلاس فراوان هستند اما نژاد ژرمن شپرد بیشتر در اروپا زندگی دیده شدهاست. مهمترین ویژگی در این نژاد رفتارهای اشرافی، شهامت و توانایی آموختن رفتارها و فعالیتهای اختصاصی است. نخستین ویژگی یک جرمن شپرد خوب، قدرت، چالاکی، عضلات مناسب و هوشیاری است. رنگ در سگهای ژرمن شپرد متفاوت است و تقریبا اکثر رنگها قابل قبول هستند. با این وجود رنگهای خیلی کم رنگ یا سفید یک دست قابل قبول نمیباشد. پرسش: عمر سگ ژرمن شپرد چند ساله؟ پاسخ:"
---
# GPT2 QA - Persian
It is a new approach to using GPT2 in other downstream NLP tasks like QA. The model was trained on PersianQA and evaluated on PersianQA and PersiNLU (Reading Comprehension).
## Dataset
- [PersianQA](https://github.com/sajjjadayobi/PersianQA)
- [ParsiNLU](https://github.com/persiannlp/parsinlu)
## Evaluation
The following table summarizes the scores obtained by the model.
| Dataset | F1 Score (%) | Exact Match (%) | Total (#) |
|:---------:|:------------:|:---------------:|:---------:|
| ParsNLU | 46.95 | 20.39 | 564 |
| PersianQA | 45.93 | 23.19 | 651 |
## Demo
[Streamlit GPT2 QA - Persian](https://huggingface.co/spaces/m3hrdadfi/gpt2-persian-qa)
## How to use
TODO (will be filled shortly)...
|
seongju/squadv2-xlm-roberta-base
|
seongju
| 2021-07-30T07:45:30Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
### Model information
* language : English
* fine tuning data : [squad 2.0](https://rajpurkar.github.io/SQuAD-explorer/)
* License : CC-BY-SA 4.0
* Base model : [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)
* input : question, context
* output : answer
----
### Train information
* train_runtime : 7562.859
* train_steps_per_second : 1.077
* training_loss : 0.9661213896603117
* epoch: 3.0
----
### How to use
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained (
"seongju/squadv2-xlm-roberta-base"
)
model = AutoModelForSequenceClassification.from_pretrained (
"seongju/squadv2-xlm-roberta-base"
)
```
|
huggingtweets/deepleffen-ibnalrafidayn
|
huggingtweets
| 2021-07-30T07:03:57Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/deepleffen-ibnalrafidayn/1627628633670/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1241879678455078914/e2EdZIrr_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1419130410659889153/F2F8J5kC_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Deep Leffen Bot & ابن ڪربلاء 🇮🇶🇵🇸</div>
<div style="text-align: center; font-size: 14px;">@deepleffen-ibnalrafidayn</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Deep Leffen Bot & ابن ڪربلاء 🇮🇶🇵🇸.
| Data | Deep Leffen Bot | ابن ڪربلاء 🇮🇶🇵🇸 |
| --- | --- | --- |
| Tweets downloaded | 497 | 3157 |
| Retweets | 13 | 1624 |
| Short tweets | 26 | 149 |
| Tweets kept | 458 | 1384 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/nxq13p47/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @deepleffen-ibnalrafidayn's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/40mnb9ye) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/40mnb9ye/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/deepleffen-ibnalrafidayn')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
pranav1015/distilbert-base-uncased-finetuned-cola
|
pranav1015
| 2021-07-30T05:27:22Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model_index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metric:
name: Matthews Correlation
type: matthews_correlation
value: 0.520875943143754
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8486
- Matthews Correlation: 0.5209
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5265 | 1.0 | 535 | 0.5479 | 0.4049 |
| 0.3571 | 2.0 | 1070 | 0.5002 | 0.5164 |
| 0.2432 | 3.0 | 1605 | 0.6242 | 0.5091 |
| 0.173 | 4.0 | 2140 | 0.7559 | 0.5120 |
| 0.1352 | 5.0 | 2675 | 0.8486 | 0.5209 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
avneet/distilbert-base-uncased-finetuned-cola
|
avneet
| 2021-07-30T00:15:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model_index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metric:
name: Matthews Correlation
type: matthews_correlation
value: 0.42176824452830747
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4981
- Matthews Correlation: 0.4218
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5248 | 1.0 | 535 | 0.4981 | 0.4218 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
huggingtweets/flower_zaddy
|
huggingtweets
| 2021-07-29T23:30:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/flower_zaddy/1627601426529/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1414421050415329283/SnA_5soV_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">label stacker</div>
<div style="text-align: center; font-size: 14px;">@flower_zaddy</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from label stacker.
| Data | label stacker |
| --- | --- |
| Tweets downloaded | 992 |
| Retweets | 209 |
| Short tweets | 119 |
| Tweets kept | 664 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/qsem7akp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @flower_zaddy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/c2jwdb2x) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/c2jwdb2x/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/flower_zaddy')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
veronica320/QA-for-Event-Extraction
|
veronica320
| 2021-07-29T22:57:42Z | 28 | 7 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
# QA-for-Event-Extraction
## Model description
This is a QA model as part of the event extraction system in the ACL2021 paper: [Zero-shot Event Extraction via Transfer Learning: Challenges and Insights](https://aclanthology.org/2021.acl-short.42/). The pretrained architecture is [roberta-large](https://huggingface.co/roberta-large) and the fine-tuning data is [QAMR](https://github.com/uwnlp/qamr).
## Demo
To see how the model works, type a question and a context separated in the right-hand-side textboxs under "Hosted inference API".
Example:
- Question: `Who was killed?`
- Context: `A car bomb exploded Thursday in a crowded outdoor market in the heart of Jerusalem, killing at least two people, police said.`
- Answer: `people`
## Usage
- To use the QA model independently, follow the [huggingface documentation on AutoModelForQuestionAnswering](https://huggingface.co/transformers/task_summary.html?highlight=automodelforquestionanswering#extractive-question-answering).
- To use it as part of the event extraction system, please check out [our Github repo](https://github.com/veronica320/Zeroshot-Event-Extraction).
### BibTeX entry and citation info
```
@inproceedings{lyu-etal-2021-zero,
title = "Zero-shot Event Extraction via Transfer Learning: {C}hallenges and Insights",
author = "Lyu, Qing and
Zhang, Hongming and
Sulem, Elior and
Roth, Dan",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.42",
doi = "10.18653/v1/2021.acl-short.42",
pages = "322--332",
abstract = "Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies. In this work, we explore the possibility of zero-shot event extraction by formulating it as a set of Textual Entailment (TE) and/or Question Answering (QA) queries (e.g. {``}A city was attacked{''} entails {``}There is an attack{''}), exploiting pretrained TE/QA models for direct transfer. On ACE-2005 and ERE, our system achieves acceptable results, yet there is still a large gap from supervised approaches, showing that current QA and TE technologies fail in transferring to a different domain. To investigate the reasons behind the gap, we analyze the remaining key challenges, their respective impact, and possible improvement directions.",
}
```
|
huggingtweets/islamphobiacow
|
huggingtweets
| 2021-07-29T22:31:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/islamphobiacow/1627597861566/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1368077075127603200/Z08slO2P_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">beff jezos</div>
<div style="text-align: center; font-size: 14px;">@islamphobiacow</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from beff jezos.
| Data | beff jezos |
| --- | --- |
| Tweets downloaded | 395 |
| Retweets | 36 |
| Short tweets | 37 |
| Tweets kept | 322 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1crtakdb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @islamphobiacow's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/29lljwti) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/29lljwti/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/islamphobiacow')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/guywiththepie
|
huggingtweets
| 2021-07-29T15:40:25Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/guywiththepie/1627573203188/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1332093894218182659/-dCbl61O_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">GuyWithThePie (🎂 in 1 week)</div>
<div style="text-align: center; font-size: 14px;">@guywiththepie</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from GuyWithThePie (🎂 in 1 week).
| Data | GuyWithThePie (🎂 in 1 week) |
| --- | --- |
| Tweets downloaded | 3204 |
| Retweets | 445 |
| Short tweets | 422 |
| Tweets kept | 2337 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1lir19ia/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @guywiththepie's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ru7uv7v) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ru7uv7v/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/guywiththepie')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Zane/Ricky3
|
Zane
| 2021-07-29T14:50:17Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
# DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) trained on a game character, Neku Sakuraba from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script).
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-small-neku")
model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-small-neku")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("NekuBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
|
Geotrend/distilbert-base-en-ro-cased
|
Geotrend
| 2021-07-29T11:21:02Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-en-ro-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-ro-cased")
model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-ro-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermdistilbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact amine@geotrend.fr for any question, feedback or request.
|
Geotrend/distilbert-base-en-da-cased
|
Geotrend
| 2021-07-29T10:29:09Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-en-da-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-da-cased")
model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-da-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermdistilbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact amine@geotrend.fr for any question, feedback or request.
|
andi611/distilbert-base-uncased-squad2-with-ner-with-neg-with-multi
|
andi611
| 2021-07-29T03:14:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- conll2003
model_index:
- name: distilbert-base-uncased-squad2-with-ner-with-neg-with-multi
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: conll2003
type: conll2003
args: conll2003
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi
This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
huggingtweets/thebabylonbee-theonion
|
huggingtweets
| 2021-07-29T00:04:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/thebabylonbee-theonion/1627517094487/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/875392068125769732/yrN-1k0Y_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1400100770624720898/-HC7kL5x_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">The Onion & The Babylon Bee</div>
<div style="text-align: center; font-size: 14px;">@thebabylonbee-theonion</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from The Onion & The Babylon Bee.
| Data | The Onion | The Babylon Bee |
| --- | --- | --- |
| Tweets downloaded | 3250 | 3249 |
| Retweets | 8 | 243 |
| Short tweets | 13 | 15 |
| Tweets kept | 3229 | 2991 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ueetvmn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @thebabylonbee-theonion's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1g46l1ro) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1g46l1ro/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/thebabylonbee-theonion')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/dril-theonion
|
huggingtweets
| 2021-07-28T23:56:37Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/dril-theonion/1627516593101/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/875392068125769732/yrN-1k0Y_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">The Onion & wint</div>
<div style="text-align: center; font-size: 14px;">@dril-theonion</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from The Onion & wint.
| Data | The Onion | wint |
| --- | --- | --- |
| Tweets downloaded | 3250 | 3229 |
| Retweets | 8 | 466 |
| Short tweets | 13 | 311 |
| Tweets kept | 3229 | 2452 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3efeq3yq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dril-theonion's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3mrv8gkj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3mrv8gkj/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/dril-theonion')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/drilbot_neo-rusticgendarme
|
huggingtweets
| 2021-07-28T19:24:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/drilbot_neo-rusticgendarme/1627500242288/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1405236436144508932/5bN_yThT_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374924360780242944/-Q8NfgEr_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">merzy & wintbot_neo</div>
<div style="text-align: center; font-size: 14px;">@drilbot_neo-rusticgendarme</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from merzy & wintbot_neo.
| Data | merzy | wintbot_neo |
| --- | --- | --- |
| Tweets downloaded | 2598 | 3244 |
| Retweets | 449 | 218 |
| Short tweets | 440 | 271 |
| Tweets kept | 1709 | 2755 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/33n6vv8i/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @drilbot_neo-rusticgendarme's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ti3qa9s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ti3qa9s/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/drilbot_neo-rusticgendarme')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Geotrend/distilbert-base-en-fr-nl-ru-ar-cased
|
Geotrend
| 2021-07-28T15:56:18Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-en-fr-nl-ru-ar-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-fr-nl-ru-ar-cased")
model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-fr-nl-ru-ar-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermdistilbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact amine@geotrend.fr for any question, feedback or request.
|
junnyu/roformer_paddle
|
junnyu
| 2021-07-28T14:47:01Z | 0 | 0 | null |
[
"paddlepaddle",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# paddle paddle版本的RoFormer
# 需要安装最新的paddlenlp
`pip install git+https://github.com/PaddlePaddle/PaddleNLP.git`
## 预训练模型转换
预训练模型可以从 huggingface/transformers 转换而来，方法如下（适用于roformer模型，其他模型按情况调整）：
1. 从huggingface.co获取roformer模型权重
2. 设置参数运行convert.py代码
3. 例子：
假设我想转换https://huggingface.co/junnyu/roformer_chinese_base 权重
- (1)首先下载 https://huggingface.co/junnyu/roformer_chinese_base/tree/main 中的pytorch_model.bin文件,假设我们存入了`./roformer_chinese_base/pytorch_model.bin`
- (2)运行convert.py
```bash
python convert.py \
--pytorch_checkpoint_path ./roformer_chinese_base/pytorch_model.bin \
--paddle_dump_path ./roformer_chinese_base/model_state.pdparams
```
- (3)最终我们得到了转化好的权重`./roformer_chinese_base/model_state.pdparams`
## 预训练MLM测试
### test_mlm.py
```python
import paddle
import argparse
from paddlenlp.transformers import RoFormerForPretraining, RoFormerTokenizer
def test_mlm(text, model_name):
model = RoFormerForPretraining.from_pretrained(model_name)
model.eval()
tokenizer = RoFormerTokenizer.from_pretrained(model_name)
tokens = ["[CLS]"]
text_list = text.split("[MASK]")
for i,t in enumerate(text_list):
tokens.extend(tokenizer.tokenize(t))
if i==len(text_list)-1:
tokens.extend(["[SEP]"])
else:
tokens.extend(["[MASK]"])
input_ids_list = tokenizer.convert_tokens_to_ids(tokens)
input_ids = paddle.to_tensor([input_ids_list])
with paddle.no_grad():
pd_outputs = model(input_ids)[0][0]
pd_outputs_sentence = "paddle: "
for i, id in enumerate(input_ids_list):
if id == tokenizer.convert_tokens_to_ids(["[MASK]"])[0]:
tokens = tokenizer.convert_ids_to_tokens(pd_outputs[i].topk(5)[1].tolist())
pd_outputs_sentence += "[" + "||".join(tokens) + "]"
else:
pd_outputs_sentence += "".join(
tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True)
)
print(pd_outputs_sentence)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_name", default="roformer-chinese-base", type=str, help="Pretrained roformer name or path."
)
parser.add_argument(
"--text", default="今天[MASK]很好，我想去公园玩！", type=str, help="MLM text."
)
args = parser.parse_args()
test_mlm(text=args.text, model_name=args.model_name)
```
### 输出
```bash
python test_mlm.py --model_name roformer-chinese-base --text 今天[MASK]很好，我想去公园玩！
# paddle: 今天[天气||天||阳光||太阳||空气]很好，我想去公园玩！
python test_mlm.py --model_name roformer-chinese-base --text 北京是[MASK]的首都！
# paddle: 北京是[中国||谁||中华人民共和国||我们||中华民族]的首都！
python test_mlm.py --model_name roformer-chinese-char-base --text 今天[MASK]很好，我想去公园玩！
# paddle: 今天[天||气||都||风||人]很好，我想去公园玩！
python test_mlm.py --model_name roformer-chinese-char-base --text 北京是[MASK]的首都！
# paddle: 北京是[谁||我||你||他||国]的首都！
```
|
Geotrend/distilbert-base-en-fr-uk-el-ro-cased
|
Geotrend
| 2021-07-28T13:34:16Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-en-fr-uk-el-ro-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-fr-uk-el-ro-cased")
model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-fr-uk-el-ro-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermdistilbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact amine@geotrend.fr for any question, feedback or request.
|
ml6team/byt5-base-dutch-ocr-correction
|
ml6team
| 2021-07-28T11:32:17Z | 42 | 10 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# ByT5 Dutch OCR Correction
This model is a finetuned byT5 model that corrects OCR mistakes found in dutch sentences. The [google/byt5-base](https://huggingface.co/google/byt5-base) model is finetuned on the dutch section of the [OSCAR](https://huggingface.co/datasets/oscar) dataset.
## Usage
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
example_sentence = "Ben algoritme dat op ba8i8 van kunstmatige inte11i9entie vkijwel geautomatiseerd een tekst herstelt met OCR fuuten."
tokenizer = AutoTokenizer.from_pretrained('ml6team/byt5-base-dutch-ocr-correction')
model_inputs = tokenizer(example_sentence, max_length=128, truncation=True, return_tensors="pt")
model = T5ForConditionalGeneration.from_pretrained('ml6team/byt5-base-dutch-ocr-correction')
outputs = model.generate(**model_inputs, max_length=128)
tokenizer.decode(outputs[0])
```
|
eugenesiow/msrn-bam
|
eugenesiow
| 2021-07-28T10:12:49Z | 60 | 5 |
transformers
|
[
"transformers",
"MSRN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- super-image
- image-super-resolution
datasets:
- eugenesiow/Div2k
- eugenesiow/Set5
- eugenesiow/Set14
- eugenesiow/BSD100
- eugenesiow/Urban100
metrics:
- pnsr
- ssim
---
# Multi-scale Residual Network for Image Super-Resolution (MSRN)
MSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Multi-scale Residual Network for Image Super-Resolution](https://openaccess.thecvf.com/content_ECCV_2018/html/Juncheng_Li_Multi-scale_Residual_Network_ECCV_2018_paper.html) by Li et al. (2018) and first released in [this repository](https://github.com/MIVRC/MSRN-PyTorch).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and model upscaling x2.
## Model description
The MSRN model proposes a feature extraction structure called the multi-scale residual block. This module can "adaptively detect image features at different scales" and "exploit the potential features of the image".
This model also applies the balanced attention (BAM) method invented by [Wang et al. (2021)](https://arxiv.org/abs/2104.07566) to further improve the results.
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import MsrnModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = MsrnModel.from_pretrained('eugenesiow/msrn-bam', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, MsrnModel, MsrnConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = MsrnConfig(
scale=4, # train a model to upscale 4x
bam=True, # apply balanced attention to the network
)
model = MsrnModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |msrn-bam |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**38.02/0.9608** |
|Set5 |3x |30.39/0.8678 |**35.13/0.9408** |
|Set5 |4x |28.42/0.8101 |**32.26/0.8955** |
|Set14 |2x |30.22/0.8683 |**33.73/0.9186** |
|Set14 |3x |27.53/0.7737 |**31.06/0.8588** |
|Set14 |4x |25.99/0.7023 |**28.78/0.7859** |
|BSD100 |2x |29.55/0.8425 |**33.78/0.9253** |
|BSD100 |3x |27.20/0.7382 |**29.65/0.8196** |
|BSD100 |4x |25.96/0.6672 |**28.51/0.7651** |
|Urban100 |2x |26.66/0.8408 |**32.08/0.9276** |
|Urban100 |3x | |**29.26/0.8736** |
|Urban100 |4x |23.14/0.6573 |**26.10/0.7857** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@misc{wang2021bam,
title={BAM: A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution},
author={Fanyi Wang and Haotian Hu and Cheng Shen},
year={2021},
eprint={2104.07566},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
```bibtex
@InProceedings{Li_2018_ECCV,
author = {Li, Juncheng and Fang, Faming and Mei, Kangfu and Zhang, Guixu},
title = {Multi-scale Residual Network for Image Super-Resolution},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}
```
|
huggingtweets/chexmixfan8
|
huggingtweets
| 2021-07-28T08:57:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/chexmixfan8/1627462638364/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1364595665830092803/P58O-LXT_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">based king????</div>
<div style="text-align: center; font-size: 14px;">@chexmixfan8</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from based king????.
| Data | based king???? |
| --- | --- |
| Tweets downloaded | 550 |
| Retweets | 103 |
| Short tweets | 111 |
| Tweets kept | 336 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3gad2ytk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @chexmixfan8's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2zbryz29) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2zbryz29/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/chexmixfan8')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Geotrend/distilbert-base-en-fr-de-no-da-cased
|
Geotrend
| 2021-07-28T07:52:56Z | 199 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-en-fr-de-no-da-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-fr-de-no-da-cased")
model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-fr-de-no-da-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermdistilbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact amine@geotrend.fr for any question, feedback or request.
|
Alireza1044/albert-base-v2-wnli
|
Alireza1044
| 2021-07-28T07:30:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model_index:
- name: wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
args: wnli
metric:
name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6898
- Accuracy: 0.5634
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
MYX4567/distilgpt2-finetuned-wikitext2
|
MYX4567
| 2021-07-28T06:37:12Z | 868 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model_index:
- name: distilgpt2-finetuned-wikitext2
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6428
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.76 | 1.0 | 2334 | 3.6658 |
| 3.6325 | 2.0 | 4668 | 3.6454 |
| 3.6068 | 3.0 | 7002 | 3.6428 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
huggingtweets/mickyrourk
|
huggingtweets
| 2021-07-28T04:38:39Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/mickyrourk/1627447046714/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1419815374904770561/Qal6NB91_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Micky Rourk</div>
<div style="text-align: center; font-size: 14px;">@mickyrourk</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Micky Rourk.
| Data | Micky Rourk |
| --- | --- |
| Tweets downloaded | 2139 |
| Retweets | 194 |
| Short tweets | 73 |
| Tweets kept | 1872 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1buf25n4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mickyrourk's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2thme21b) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2thme21b/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/mickyrourk')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/drwrightquotes-nickszabo4-s__nakamoto
|
huggingtweets
| 2021-07-28T03:53:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/drwrightquotes-nickszabo4-s__nakamoto/1627444323672/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/677459045918314496/satUWUbV_400x400.png')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1256199289476272131/JWhrljdS_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1362597154578075648/2WBy5DJd_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Dorian Nakamoto & Craig Wright Quotes & Nick Szabo</div>
<div style="text-align: center; font-size: 14px;">@drwrightquotes-nickszabo4-s__nakamoto</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Dorian Nakamoto & Craig Wright Quotes & Nick Szabo.
| Data | Dorian Nakamoto | Craig Wright Quotes | Nick Szabo |
| --- | --- | --- | --- |
| Tweets downloaded | 3166 | 316 | 3121 |
| Retweets | 1419 | 0 | 1519 |
| Short tweets | 650 | 62 | 71 |
| Tweets kept | 1097 | 254 | 1531 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/18sunueo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @drwrightquotes-nickszabo4-s__nakamoto's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3203umr9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3203umr9/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/drwrightquotes-nickszabo4-s__nakamoto')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Alireza1044/albert-base-v2-qqp
|
Alireza1044
| 2021-07-28T02:04:17Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model_index:
- name: qqp
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
args: qqp
metric:
name: F1
type: f1
value: 0.8722569490623753
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qqp
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3695
- Accuracy: 0.9050
- F1: 0.8723
- Combined Score: 0.8886
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
Alireza1044/albert-base-v2-mnli
|
Alireza1044
| 2021-07-27T21:10:33Z | 51 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model_index:
- name: mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metric:
name: Accuracy
type: accuracy
value: 0.8500813669650122
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5383
- Accuracy: 0.8501
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
Geotrend/distilbert-base-en-pl-cased
|
Geotrend
| 2021-07-27T18:59:24Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-en-pl-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-pl-cased")
model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-pl-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermdistilbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact amine@geotrend.fr for any question, feedback or request.
|
osanseviero/hubert-sd
|
osanseviero
| 2021-07-27T18:04:54Z | 0 | 0 |
superb
|
[
"superb",
"speaker-diarization",
"benchmark:superb",
"speech-segmentation",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- superb
- speaker-diarization
- benchmark:superb
library_name: superb
pipeline_tag: speech-segmentation
---
# Test for superb using hubert downstream SD
## Usage
```python
import io
import soundfile as sf
from urllib.request import urlopen
from model import PreTrainedModel
model = PreTrainedModel()
url = "https://huggingface.co/datasets/lewtun/s3prl-sd-dummy/raw/main/audio.wav"
data, samplerate = sf.read(io.BytesIO(urlopen(url).read()))
print(model(data))
```
|
roschmid/dog-races
|
roschmid
| 2021-07-27T17:32:40Z | 70 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: dog-races-v2
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8299999833106995
---
# dog-races-v2
Autogenerated Model created thannks to HuggingPics🤗🖼️. You can create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
This Model is an improvement to my last model, where the Chow Chow data included images of American pickles with the same name (contaminated data).
Current labels are: 1) Border Collie, 2) Chow Chow, 3) German Shepherd, 4) Golden Retriever, 5) Pug, 6) Rottweiler, 7) Shiba Inu, 8) Siberian Husky and 9) Tibetan Mastiff.
There is still room for improvement.
Model Accuracy: 82.99%
When tested with Stanford Dogs Dataset, these were the results:
- Golden Retriever: 90% (117/130 images labeled correctly)
- Chow Chow: 97.45% (191/196 images labeled correctly)
- Tibetan Mastiff: 12.5% (19/152 images labeled correctly). Probably some issue with the data (most were labeled as Chow Chow).
## Example Images
#### Border Collie
#### Chow Chow dog
#### German Shepherd
#### Golden Retriever
#### Pug
#### Rottweiler
#### Shiba Inu
#### Siberian Husky
#### Tibetan Mastiff
|
Geotrend/distilbert-base-en-fr-da-ja-vi-cased
|
Geotrend
| 2021-07-27T16:28:47Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-en-fr-da-ja-vi-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-fr-da-ja-vi-cased")
model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-fr-da-ja-vi-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermdistilbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact amine@geotrend.fr for any question, feedback or request.
|
Alireza1044/albert-base-v2-qnli
|
Alireza1044
| 2021-07-27T15:39:56Z | 54 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model_index:
- name: qnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metric:
name: Accuracy
type: accuracy
value: 0.9137836353651839
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3608
- Accuracy: 0.9138
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
amankhandelia/panini
|
amankhandelia
| 2021-07-27T07:13:15Z | 6 | 0 |
transformers
|
[
"transformers",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
widget:
- text: "मुझे उनसे बात करना <mask> अच्छा लगा"
- text: "हम आपके सुखद <mask> की कामना करते हैं"
- text: "सभी अच्छी चीजों का एक <mask> होता है"
---
# RoBERTa base model for Hindi language
Pretrained model on Hindi language using a masked language modeling (MLM) objective. [A more interactive & comparison demo is available here](https://huggingface.co/spaces/flax-community/roberta-hindi).
> This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/pretrain-roberta-from-scratch-in-hindi/7091), organized by [Hugging Face](https://huggingface.co/) and TPU usage sponsored by Google.
## Model description
RoBERTa Hindi is a transformers model pretrained on a large corpus of Hindi data(a combination of **mc4, oscar and indic-nlp** datasets)
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='flax-community/roberta-hindi')
>>> unmasker("हम आपके सुखद <mask> की कामना करते हैं")
[{'score': 0.3310680091381073,
'sequence': 'हम आपके सुखद सफर की कामना करते हैं',
'token': 1349,
'token_str': ' सफर'},
{'score': 0.15317578613758087,
'sequence': 'हम आपके सुखद पल की कामना करते हैं',
'token': 848,
'token_str': ' पल'},
{'score': 0.07826550304889679,
'sequence': 'हम आपके सुखद समय की कामना करते हैं',
'token': 453,
'token_str': ' समय'},
{'score': 0.06304813921451569,
'sequence': 'हम आपके सुखद पहल की कामना करते हैं',
'token': 404,
'token_str': ' पहल'},
{'score': 0.058322224766016006,
'sequence': 'हम आपके सुखद अवसर की कामना करते हैं',
'token': 857,
'token_str': ' अवसर'}]
```
## Training data
The RoBERTa Hindi model was pretrained on the reunion of the following datasets:
- [OSCAR](https://huggingface.co/datasets/oscar) is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
- [mC4](https://huggingface.co/datasets/mc4) is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus.
- [IndicGLUE](https://indicnlp.ai4bharat.org/indic-glue/) is a natural language understanding benchmark.
- [Samanantar](https://indicnlp.ai4bharat.org/samanantar/) is a parallel corpora collection for Indic language.
- [Hindi Text Short and Large Summarization Corpus](https://www.kaggle.com/disisbig/hindi-text-short-and-large-summarization-corpus) is a collection of ~180k articles with their headlines and summary collected from Hindi News Websites.
- [Hindi Text Short Summarization Corpus](https://www.kaggle.com/disisbig/hindi-text-short-summarization-corpus) is a collection of ~330k articles with their headlines collected from Hindi News Websites.
- [Old Newspapers Hindi](https://www.kaggle.com/crazydiv/oldnewspapershindi) is a cleaned subset of HC Corpora newspapers.
## Training procedure
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`.
- We had to perform cleanup of **mC4** and **oscar** datasets by removing all non hindi (non Devanagari) characters from the datasets.
- We tried to filter out evaluation set of WikiNER of [IndicGlue](https://indicnlp.ai4bharat.org/indic-glue/) benchmark by [manual labelling](https://github.com/amankhandelia/roberta_hindi/blob/master/wikiner_incorrect_eval_set.csv) where the actual labels were not correct and modifying the [downstream evaluation dataset](https://github.com/amankhandelia/roberta_hindi/blob/master/utils.py).
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores).A randomized shuffle of combined dataset of **mC4, oscar** and other datasets listed above was used to train the model. Training logs are present in [wandb](https://wandb.ai/wandb/hf-flax-roberta-hindi).
## Evaluation Results
RoBERTa Hindi is evaluated on various downstream tasks. The results are summarized below.
| Task | Task Type | IndicBERT | HindiBERTa | Indic Transformers Hindi BERT | RoBERTa Hindi Guj San | RoBERTa Hindi |
|-------------------------|----------------------|-----------|------------|-------------------------------|-----------------------|---------------|
| BBC News Classification | Genre Classification | **76.44** | 66.86 | **77.6** | 64.9 | 73.67 |
| WikiNER | Token Classification | - | 90.68 | **95.09** | 89.61 | **92.76** |
| IITP Product Reviews | Sentiment Analysis | **78.01** | 73.23 | **78.39** | 66.16 | 75.53 |
| IITP Movie Reviews | Sentiment Analysis | 60.97 | 52.26 | **70.65** | 49.35 | **61.29** |
## Team Members
- Aman K ([amankhandelia](https://huggingface.co/amankhandelia))
- Haswanth Aekula ([hassiahk](https://huggingface.co/hassiahk))
- Kartik Godawat ([dk-crazydiv](https://huggingface.co/dk-crazydiv))
- Prateek Agrawal ([prateekagrawal](https://huggingface.co/prateekagrawal))
- Rahul Dev ([mlkorra](https://huggingface.co/mlkorra))
## Credits
Huge thanks to Hugging Face 🤗 & Google Jax/Flax team for such a wonderful community week, especially for providing such massive computing resources. Big thanks to [Suraj Patil](https://huggingface.co/valhalla) & [Patrick von Platen](https://huggingface.co/patrickvonplaten) for mentoring during the whole week.
<img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:medium>
|
Edomonndo/opus-mt-en-ro-finetuned-en-to-ro
|
Edomonndo
| 2021-07-27T05:34:02Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model_index:
- name: opus-mt-en-ro-finetuned-en-to-ro
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt16
type: wmt16
args: ro-en
metric:
name: Bleu
type: bleu
value: 28.1641
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2886
- Bleu: 28.1641
- Gen Len: 34.1071
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.7436 | 1.0 | 38145 | 1.2886 | 28.1641 | 34.1071 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
Geotrend/distilbert-base-en-uk-cased
|
Geotrend
| 2021-07-26T21:26:11Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-en-uk-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-uk-cased")
model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-uk-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermdistilbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact amine@geotrend.fr for any question, feedback or request.
|
vasudevgupta/bigbird-roberta-base
|
vasudevgupta
| 2021-07-26T17:30:39Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"big_bird",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
Moved here: https://huggingface.co/google/bigbird-roberta-base
|
nishmithaur/distilbert-base-uncased-finetuned-ner
|
nishmithaur
| 2021-07-26T14:59:51Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
model_index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0623
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2377 | 1.0 | 878 | 0.0711 |
| 0.0514 | 2.0 | 1756 | 0.0637 |
| 0.031 | 3.0 | 2634 | 0.0623 |
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
flax-community/gpt-neo-125M-code-clippy
|
flax-community
| 2021-07-26T14:07:46Z | 13 | 10 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt_neo",
"text-generation",
"arxiv:2107.03374",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# GPT-Neo-125M-Code-Clippy
> **Please refer to our new [GitHub Wiki](https://github.com/ncoop57/gpt-code-clippy/wiki) which documents our efforts in detail in creating the open source version of GitHub Copilot**
## Model Description
GPT-Neo-125M-Code-Clippy is a [GPT-Neo-125M model](https://huggingface.co/EleutherAI/gpt-neo-125M) finetuned using causal language modeling on our version of the Code Clippy Data dataset that has duplicates, which was scraped from public Github repositories (more information in the provided link). This model is specialized to autocomplete methods in multiple programming languages. As discussed in OpenAI's [Codex paper](https://arxiv.org/abs/2107.03374), we modified the GPT-Neo model and tokenizer to accommodate for additional whitespace characters. Specifically, we add the following tokens `["\t\t", " ", " ", " "]` and since they are all related to indentation, we initialize the embedding layer of these tokens with the same weights as the `\t` token already present in the model in hopes the model will learn to associate these whitespace characters with indentation faster. A script to automatically do this can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/training/utilities/add_new_tokens.py).
## Training data
[Code Clippy Data dataset](https://the-eye.eu/public/AI/training_data/code_clippy_data/code_clippy_dedup_data/).
## Training procedure
The training script used to train this model can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/training/run_clm_streaming_flax.py).
To reproduce the training one can use this command with the above script:
```bash
./run_clm_streaming_flax.py \
--output_dir $HOME/gpt-neo-125M-code-clippy \
--model_name_or_path="flax-community/gpt-neo-125M-code-clippy" \
--dataset_name $HOME/gpt-code-clippy/data_processing/code_clippy.py \
--data_dir /home/shared/code_clippy_data \
--text_column_name="text" \
--do_train --do_eval \
--block_size="2048" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="16" \
--preprocessing_num_workers="8" \
--learning_rate="1e-4" \
--max_steps 100000 \
--warmup_steps 2500 \
--decay_steps 25000 \
--adam_beta1="0.9" \
--adam_beta2="0.95" \
--weight_decay="0.1" \
--overwrite_output_dir \
--logging_steps="100" \
--eval_steps="500" \
--push_to_hub="False" \
--report_to="all" \
--dtype="bfloat16" \
--skip_memory_metrics="True" \
--save_steps="500" \
--save_total_limit 10 \
--gradient_accumulation_steps 16 \
--report_to="wandb" \
--run_name="125m_1e-4lr_1024bs" \
--max_eval_samples 2000 \
--save_optimizer true
```
## Intended Use and Limitations
The model is finetuned on text files from github repositories (mostly programming languages but also markdown and other project related files).
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, FlaxAutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("flax-community/gpt-neo-125M-code-clippy")
tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt-neo-125M-code-clippy")
prompt = """def greet(name):
'''A function to greet user. Given a user name it should say hello'''
"""
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device)
start = input_ids.size(1)
out = model.generate(input_ids, do_sample=True, max_length=50, num_beams=2,
early_stopping=True, eos_token_id=tokenizer.eos_token_id, )
print(tokenizer.decode(out[0][start:]))
```
### Limitations and Biases
The model is intended to be used for research purposes and comes with no guarantees of quality of generated code.
The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discuss are highlighted here as it pertains to this dataset and models that may be trained from it. **As well as some differences in views from the paper, particularly around legal implications**.
1. **Over-reliance:** This model may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using this language model.
2. **Economic and labor market impacts:** Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from [O*NET OnLine](https://www.onetonline.org/link/summary/15-1252.00), developers don't just write software.
3. **Security implications:** No filtering or checking of vulnerabilities or buggy code was performed on the datase this model is trained on. This means that the dataset may contain code that may be malicious or contain vulnerabilities. Therefore, this model may generate vulnerable, buggy, or malicious code. In safety critical software, this could lead to software that may work improperly and could result in serious consequences depending on the software. Additionally, this model may be able to be used to generate malicious code on purpose in order to perform ransomware or other such attacks.
4. **Legal implications:** No filtering was performed on licensed code. This means that the dataset may contain restrictive licensed code. As discussed in the paper, public Github repositories may fall under "fair use." However, there has been little to no previous cases of such usages of licensed publicly available code. Therefore, any code generated with this model may be required to obey license terms that align with the software it was trained on such as GPL-3.0. It is unclear the legal ramifications of using a language model trained on this dataset.
5. **Biases:** The programming languages most represented in the dataset this model was trained on are Javascript and Python. Therefore, other, still popular languages such as C and C++, are less represented and therefore the models performance for these languages will be less comparatively. Additionally, this dataset only contains public repositories and so the model may not generate code that is representative of code written by private developers. No filtering was performed for potential racist, offensive, or otherwise inappropriate content. Therefore, this model may reflect such biases in its generation.
GPT-Neo-125M-Code-Clippy is finetuned from GPT-Neo and might have inherited biases and limitations from it. See [GPT-Neo model card](https://huggingface.co/EleutherAI/gpt-neo-125M#limitations-and-biases) for details.
## Eval results
Coming soon...
|
flax-community/gpt-neo-125M-code-clippy-dedup
|
flax-community
| 2021-07-26T14:07:29Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt_neo",
"text-generation",
"arxiv:2107.03374",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# GPT-Neo-125M-Code-Clippy-Dedup
> **Please refer to our new [GitHub Wiki](https://github.com/ncoop57/gpt-code-clippy/wiki) which documents our efforts in detail in creating the open source version of GitHub Copilot**
## Model Description
PT-Neo-125M-Code-Clippy-Dedup is a [GPT-Neo-125M model](https://huggingface.co/EleutherAI/gpt-neo-125M) finetuned using causal language modeling on our deduplicated version of the Code Clippy Data dataset, which was scraped from public Github repositories (more information in the provided link). This model is specialized to autocomplete methods in multiple programming languages.
## Training data
[Code Clippy Data dataset](https://huggingface.co/datasets/code_search_net).
## Training procedure
In this model's training we tried to stabilize the training by limiting the types of files we were using to train to only those that contained file extensions for popular programming languages as our dataset contains other types of files as well such as `.txt` or project configuration files. We used the following extensions to filter by:
The training script used to train this model can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/training/run_clm_streaming_filter_flax.py).
```bash
./run_clm_streaming_filter_flax.py \
--output_dir $HOME/gpt-neo-125M-code-clippy-dedup \
--model_name_or_path="EleutherAI/gpt-neo-125M" \
--dataset_name $HOME/gpt-code-clippy/data_processing/code_clippy_filter.py \
--data_dir $HOME/code_clippy_data/code_clippy_dedup_data \
--text_column_name="text" \
--do_train --do_eval \
--block_size="2048" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="16" \
--preprocessing_num_workers="8" \
--learning_rate="1e-4" \
--max_steps 100000 \
--warmup_steps 2000 \
--decay_steps 30000 \
--adam_beta1="0.9" \
--adam_beta2="0.95" \
--weight_decay="0.1" \
--overwrite_output_dir \
--logging_steps="25" \
--eval_steps="500" \
--push_to_hub="False" \
--report_to="all" \
--dtype="bfloat16" \
--skip_memory_metrics="True" \
--save_steps="500" \
--save_total_limit 10 \
--gradient_accumulation_steps 16 \
--report_to="wandb" \
--run_name="gpt-neo-125M-code-clippy-dedup-filtered-no-resize-2048bs" \
--max_eval_samples 2000 \
--save_optimizer true
```
## Intended Use and Limitations
The model is finetuned text file from github repositories (mostly programming languages but also markdown and other project related files).
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, FlaxAutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("flax-community/gpt-neo-125M-code-clippy-dedup")
tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt-neo-125M-code-clippy-dedup")
prompt = """def greet(name):
'''A function to greet user. Given a user name it should say hello'''
"""
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device)
start = input_ids.size(1)
out = model.generate(input_ids, do_sample=True, max_length=50, num_beams=2,
early_stopping=True, eos_token_id=tokenizer.eos_token_id, )
print(tokenizer.decode(out[0][start:]))
```
### Limitations and Biases
The model is intended to be used for research purposes and comes with no guarantees of quality of generated code.
The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discuss are highlighted here as it pertains to this dataset and models that may be trained from it. **As well as some differences in views from the paper, particularly around legal implications**.
1. **Over-reliance:** This model may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using this language model.
2. **Economic and labor market impacts:** Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from [O*NET OnLine](https://www.onetonline.org/link/summary/15-1252.00), developers don't just write software.
3. **Security implications:** No filtering or checking of vulnerabilities or buggy code was performed on the datase this model is trained on. This means that the dataset may contain code that may be malicious or contain vulnerabilities. Therefore, this model may generate vulnerable, buggy, or malicious code. In safety critical software, this could lead to software that may work improperly and could result in serious consequences depending on the software. Additionally, this model may be able to be used to generate malicious code on purpose in order to perform ransomware or other such attacks.
4. **Legal implications:** No filtering was performed on licensed code. This means that the dataset may contain restrictive licensed code. As discussed in the paper, public Github repositories may fall under "fair use." However, there has been little to no previous cases of such usages of licensed publicly available code. Therefore, any code generated with this model may be required to obey license terms that align with the software it was trained on such as GPL-3.0. It is unclear the legal ramifications of using a language model trained on this dataset.
5. **Biases:** The programming languages most represented in the dataset this model was trained on are Javascript and Python. Therefore, other, still popular languages such as C and C++, are less represented and therefore the models performance for these languages will be less comparatively. Additionally, this dataset only contains public repositories and so the model may not generate code that is representative of code written by private developers. No filtering was performed for potential racist, offensive, or otherwise inappropriate content. Therefore, this model may reflect such biases in its generation.
GPT-Neo-125M-Code-Clippy-Dedup is finetuned from GPT-Neo and might have inherited biases and limitations from it. See [GPT-Neo model card](https://huggingface.co/EleutherAI/gpt-neo-125M#limitations-and-biases) for details.
## Eval results
Coming soon...
|
flax-community/gpt-neo-125M-code-search-all
|
flax-community
| 2021-07-26T14:07:11Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# GPT-Code-Clippy-125M-Code-Search-All
> **Please refer to our new [GitHub Wiki](https://github.com/ncoop57/gpt-code-clippy/wiki) which documents our efforts in detail in creating the open source version of GitHub Copilot**
## Model Description
GPT-CC-125M-Code-Search is a [GPT-Neo-125M model](https://huggingface.co/EleutherAI/gpt-neo-125M) finetuned using causal language modeling on all languages in the [CodeSearchNet Challenge dataset](https://huggingface.co/datasets/code_search_net). This model is specialized to autocomplete methods in multiple programming languages.
## Training data
[CodeSearchNet Challenge dataset](https://huggingface.co/datasets/code_search_net).
## Training procedure
The training script used to train this model can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/training/run_clm_flax.py).
```bash
./run_clm_flax.py \
--output_dir $HOME/gpt-neo-125M-code-search-all \
--model_name_or_path="EleutherAI/gpt-neo-125M" \
--dataset_name code_search_net \
--dataset_config_name="all" \
--do_train --do_eval \
--block_size="512" \
--per_device_train_batch_size="32" \
--per_device_eval_batch_size="64" \
--preprocessing_num_workers="8" \
--learning_rate="1.2e-4" \
--num_train_epochs 20 \
--warmup_steps 3000 \
--adam_beta1="0.9" \
--adam_beta2="0.95" \
--weight_decay="0.1" \
--overwrite_output_dir \
--logging_steps="25" \
--eval_steps="500" \
--push_to_hub="False" \
--report_to="all" \
--dtype="bfloat16" \
--skip_memory_metrics="True" \
--save_steps="500" \
--save_total_limit 10 \
--report_to="wandb" \
--run_name="gpt-neo-125M-code-search-all"
```
## Intended Use and Limitations
The model is finetuned methods from several languages and is intended to autocomplete methods given some prompt (method signature and docstring).
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, FlaxAutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("flax-community/gpt-neo-125M-code-clippy-code-search-all")
tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt-neo-125M-code-clippy-code-search-all")
prompt = """def greet(name):
'''A function to greet user. Given a user name it should say hello'''
"""
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device)
start = input_ids.size(1)
out = model.generate(input_ids, do_sample=True, max_length=50, num_beams=2,
early_stopping=True, eos_token_id=tokenizer.eos_token_id, )
print(tokenizer.decode(out[0][start:]))
```
### Limitations and Biases
The model is intended to be used for research purposes and comes with no guarantees of quality of generated code.
GPT-CC is finetuned from GPT-Neo and might have inherited biases and limitations from it. See [GPT-Neo model card](https://huggingface.co/EleutherAI/gpt-neo-125M#limitations-and-biases) for details.
## Eval results
Coming soon...
|
flax-community/gpt-neo-125M-code-clippy-dedup-scratch
|
flax-community
| 2021-07-26T14:03:36Z | 4 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"gpt_neo",
"text-generation",
"arxiv:2107.03374",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# GPT-Code-Clippy-125M-from-Scratch
> **Please refer to our new [GitHub Wiki](https://github.com/ncoop57/gpt-code-clippy/wiki) which documents our efforts in detail in creating the open source version of GitHub Copilot**
## Model Description
GPT-CC-125M-from-Scratch is a [GPT-Neo-125M model](https://huggingface.co/EleutherAI/gpt-neo-125M) pretrained from scratch using causal language modeling on the [Code Clippy Post-deduplication dataset](https://the-eye.eu/public/AI/training_data/code_clippy_data/code_clippy_dedup_data/). The deduplication script can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/data_processing/deduplication/deduplication.py). Code Clippy was scraped from public Github repositories (more information in the provided link). This model is specialized to autocomplete methods in multiple programming languages. As discussed in OpenAI's [Codex paper](https://arxiv.org/abs/2107.03374), we modified the GPT-Neo model and tokenizer to accommodate for additional whitespace characters. Specifically, we add the following tokens `["\t\t", " ", " ", " "]` and since they are all related to indentation, we initialize the embedding layer of these tokens with the same weights as the `\t` token already present in the model in hopes the model will learn to associate these whitespace characters with indentation faster. A script to automatically do this can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/training/utilities/add_new_tokens.py).
## Training data
[Code Clippy Deduplicated dataset](https://the-eye.eu/public/AI/training_data/code_clippy_data/code_clippy_dedup_data/).
Python script of the dataset can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/data_processing/code_clippy.py)
## Training procedure
The training script used to train this model can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/training/deprecated/run_clm_streaming_flax_v2.py).
```bash
./run_clm_streaming_flax_v2.py \
--output_dir $HOME/gpt-neo-125M-code-clippy-from-scratch \
--tokenizer_name="EleutherAI/gpt-neo-125M" \
--model_name_or_path="EleutherAI/gpt-neo-125M" \
--dataset_name $HOME/gpt-code-clippy/data_processing/code_clippy.py \
--data_dir /home/shared/code_clippy_data \
--do_train --do_eval \
--block_size="2048" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="16" \
--preprocessing_num_workers="8" \
--learning_rate="3e-5" \
--max_steps 100000 \
--warmup_steps 2500\
--decap_steps 25000 \
--adam_beta1="0.9" \
--adam_beta2="0.95" \
--weight_decay="0.1" \
--overwrite_output_dir \
--logging_steps="50" \
--eval_steps="500" \
--push_to_hub="False" \
--report_to="all" \
--dtype="bfloat16" \
--skip_memory_metrics="True" \
--save_steps="500" \
--save_total_limit 10 \
--report_to="wandb" \
--run_name="gpt-neo-125M-code-clippy-dedup-scratch"
```
## Intended Use and Limitations
The model is pre-trained and not finetuned for any particular use or a particular programming language. Due to time constraints, this model was only pre-trained on 1% of the Code Clippy Dataset.
The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discuss are highlighted here as it pertains to this dataset and models that may be trained from it. **As well as some differences in views from the paper, particularly around legal implications**.
1. **Over-reliance:** This model may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using this language model.
2. **Economic and labor market impacts:** Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from [O*NET OnLine](https://www.onetonline.org/link/summary/15-1252.00), developers don't just write software.
3. **Security implications:** No filtering or checking of vulnerabilities or buggy code was performed on the datase this model is trained on. This means that the dataset may contain code that may be malicious or contain vulnerabilities. Therefore, this model may generate vulnerable, buggy, or malicious code. In safety critical software, this could lead to software that may work improperly and could result in serious consequences depending on the software. Additionally, this model may be able to be used to generate malicious code on purpose in order to perform ransomware or other such attacks.
4. **Legal implications:** No filtering was performed on licensed code. This means that the dataset may contain restrictive licensed code. As discussed in the paper, public Github repositories may fall under "fair use." However, there has been little to no previous cases of such usages of licensed publicly available code. Therefore, any code generated with this model may be required to obey license terms that align with the software it was trained on such as GPL-3.0. It is unclear the legal ramifications of using a language model trained on this dataset.
5. **Biases:** The programming languages most represented in the dataset this model was trained on are Javascript and Python. Therefore, other, still popular languages such as C and C++, are less represented and therefore the models performance for these languages will be less comparatively. Additionally, this dataset only contains public repositories and so the model may not generate code that is representative of code written by private developers. No filtering was performed for potential racist, offensive, or otherwise inappropriate content. Therefore, this model may reflect such biases in its generation.
GPT-Neo-125M-Code-Clippy is finetuned from GPT-Neo and might have inherited biases and limitations from it. See [GPT-Neo model card](https://huggingface.co/EleutherAI/gpt-neo-125M#limitations-and-biases) for details.
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, FlaxAutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("flax-community/gpt-neo-125M-code-clippy-dedup-scratch")
tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt-neo-125M-code-clippy-dedup-scratch")
prompt = """def greet(name):
'''A function to greet user. Given a user name it should say hello'''
"""
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device)
start = input_ids.size(1)
out = model.generate(input_ids, do_sample=True, max_length=50, num_beams=2,
early_stopping=True, eos_token_id=tokenizer.eos_token_id, )
print(tokenizer.decode(out[0][start:]))
```
### Limitations and Biases
The model is intended to be used for research purposes and comes with no guarantees of the quality of generated code.
GPT-CC is finetuned from GPT-Neo and might have inherited biases and limitations from it. See [GPT-Neo model card](https://huggingface.co/EleutherAI/gpt-neo-125M#limitations-and-biases) for details.
## Evaluation results
Below is a table containing the base model we started from, and the model's performance on the [HumanEval Benchmark](https://github.com/openai/human-eval).
| Model | Dataset Used | pass@1 | pass@2 | pass@5 | pass@10 |
| --- | --- | :---------: | :---------: | :---------: | :---------: |
| [gpt-neo-125M (**trained from scratch**)](https://huggingface.co/flax-community/gpt-neo-125M-code-clippy-dedup-scratch) | [Code Clippy Data (Deduplicated)](https://the-eye.eu/public/AI/training_data/code_clippy_data/code_clippy_dedup_data/) (~1% of the data) | 0.00% | 0.00% | 0.00% | 0.00% |
|
huggingtweets/unkledell
|
huggingtweets
| 2021-07-26T13:48:56Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/unkledell/1627307332006/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1199452477659238400/iMdWeVWZ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">freeuzi</div>
<div style="text-align: center; font-size: 14px;">@unkledell</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from freeuzi.
| Data | freeuzi |
| --- | --- |
| Tweets downloaded | 3220 |
| Retweets | 138 |
| Short tweets | 1159 |
| Tweets kept | 1923 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ockzquq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @unkledell's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/17ij2gx7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/17ij2gx7/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/unkledell')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Alireza1044/albert-base-v2-rte
|
Alireza1044
| 2021-07-26T12:02:09Z | 22 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model_index:
- name: rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE RTE
type: glue
args: rte
metric:
name: Accuracy
type: accuracy
value: 0.6859205776173285
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rte
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7994
- Accuracy: 0.6859
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
ForutanRad/bert-fa-QA-v1
|
ForutanRad
| 2021-07-26T03:51:47Z | 20 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"arxiv:2005.12515",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model_index:
- name: bert-fa-QA-v1
results:
- task:
name: Question Answering
type: question-answering
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-fa-QA-v1
Persian Question and answer Model Based on Bert Model
This model is a fine-tuned version of [ParsBERT](https://arxiv.org/abs/2005.12515) on PersianQA dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7297
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2563 | 1.0 | 1126 | 1.7222 |
| 1.3372 | 2.0 | 2252 | 1.7297 |
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
flax-community/clip-reply
|
flax-community
| 2021-07-26T02:11:43Z | 0 | 4 | null |
[
"tensorboard",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# Searching Reaction GIFs with CLIP
Reaction GIFs are an integral part of today's communication. They convey complex emotions with many levels, in a short compact format.
If a picture is worth a thousand words then a GIF is worth more.
We might even say that the level of complexity and expressiveness increases like:
`Emoji < Memes/Image < GIFs`
I think most people would agree it is not always easy to find the perfect reaction GIF.
Although we started out with the more ambitious goal of GIF/Image generation we later settled on first finetuning CLIP.
Which is needed to properly drive a generation model (like VQGAN).
Available CLIP models wouldn't be suitable to use without this finetuning as explained in the challenges below.
## 📝 Challenges
Classic (Image,Text) tasks like, image search, caption generation all focus on cases where the text is a description of the image.
This is mainly because large scale datasets available like COCO,WIT happen to be of that format.
So it is interesting to see if models can also capture some more higher level relations.
like sentiment-> image mapping, where there is great variation on both sides.
We can think of reaction gif/images to be sentiment like, in fact the dataset we use was also gathered for sentiment analysis.
There is no one correct reaction GIF, which also makes evaluation challenging.
# Dataset
We use the [Reaction GIF dataset](https://github.com/bshmueli/ReactionGIF) by Shmueli et al. Also available in [datasets](https://huggingface.co/datasets/julien-c/reactiongif)(without the gifs, but I already had those files, nice coincidence 😉)
We only use the tweet, GIF Response fields.
For this short experiment we only used the first frame of the GIFs to get generate the (text,image) pairs. Thought about using multiple frames from the GIF, but since there may not be much change between frames and didn't know how that would effect the already overfitting model. I decided to go with the simpler single frame version.
As opposed to other datasets, like COCO, we don't have multiple captions per image. Although same GIFs are used multiple times and we can modify the dataset that way we chose not to. Since we already had a very small dataset.
- Domain: Twitter
- Train size: 18,976
- Validation size: 351
# Model
We used the Hybrid CLIP model. Training script: [Hybrid CLIP](https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/hybrid_clip/run_hybrid_clip.py)
- Text Model: [twitter-roberta-base-emoji](https://huggingface.co/cardiffnlp/twitter-roberta-base-emoji)
- Vision Model: [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32)
## Different models tried
`cardiffnlp/twitter-roberta-base-*` models performed best compared to other text models; like the plain `roberta-base`, `bert-base-cased`.
This makes sense since this model is already trained on twitter data.
And among `cardiffnlp/twitter-roberta-base-*` models `twitter-roberta-base-emoji` performed best. (As I had hoped)
This model is `cardiffnlp/twitter-roberta-base` further fine-tuned on emoji classification task, which we can say (emojis) is a parallel to GIFs.
Also tried `google/vit-base-patch32-384`, `google/vit-base-patch16-384` for the vision models, but results were inconclusive.
## Result Interpretation (Warning)
It would be wrong to claim that this model learned 'semantic reasoning' between sentence and image features.
It more likely learned a mapping between sentence sentiment and image occurrence statistics. Because the set of gif images repeat across the dataset, although paired with different sentences.
That is not to say that learning such semantic relations isn't feasible with this model. And it is well worth working on in the future, with a larger and better constructed dataset.
### 📈 Training Logs
Training logs can be found [here](https://wandb.ai/cceyda/flax-clip?workspace=user-cceyda)
It was really easy to overfit since it was a tiny dataset. Used early stopping.
Other parameters:
```
--max_seq_length 128 \
--per_device_train_batch_size="32" \
--learning_rate="1e-5"
--warmup_steps="150"
```
# 💡 Future Potential
It is possible to generate a very large training set by scraping twitter.(Couldn't do during the event because of twitter rate limit)
I found it surprising how well the results turned out to be with so little data and training time. Although it is really hard to define what is an appropriate reaction image, and there are definite mistakes model makes.
(I also trained just a plain clip but didn't have time to prep demo for that 😅 which was also similarly over fitting and only had time to do a single run)
I will definitely be trying out training a similar model for emoji & meme data.
Training CLIP is just the first step, if we have a well trained CLIP generation is within reach 🚀
# How to use
The final model available [here](https://huggingface.co/ceyda/clip-reply)
```py
from model import FlaxHybridCLIP # see demo
from transformers import AutoTokenizer, CLIPProcessor
model = FlaxHybridCLIP.from_pretrained("ceyda/clip-reply")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
processor.tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base")
def query(image_paths,query_text):
images = [Image.open(im).convert("RGB") for im in image_paths]
inputs = processor(text=[query_text], images=images, return_tensors="jax", padding=True)
inputs["pixel_values"] = jnp.transpose(inputs["pixel_values"], axes=[0, 2, 3, 1])
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image.reshape(-1)
probs = jax.nn.softmax(logits_per_image)
```
# Created By
Ceyda Cinarel [@ceyda](https://huggingface.co/ceyda)
Made during the flax community [event](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104/58)
# TL;DR The task
Input: Some sentence (like a tweet)
Output: The most suitable reaction GIF image (Ranking)
Example:
- Input: I miss you
- Output: 
# Demo
https://huggingface.co/spaces/flax-community/clip-reply-demo
|
flax-sentence-embeddings/stackoverflow_mpnet-base
|
flax-sentence-embeddings
| 2021-07-26T01:36:33Z | 5,238 | 5 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# stackoverflow_mpnet-base
This is a microsoft/mpnet-base model trained on 18,562,443 (title, body) pairs from StackOverflow.
SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used a pretrained [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) model and trained it using Siamese Network setup and contrastive learning objective. 18,562,443 (title, body) pairs from StackOverflow was used as training data. For this model, mean pooling of hidden states were used as sentence embeddings. See data_config.json and train_script.py in this respository how the model was trained and which datasets have been used.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well
as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks.
## Intended uses
Our model is intended to be used as a sentence encoder for a search engine. Given an input sentence, it outputs a vector which captures
the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks.
## How to use
Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('flax-sentence-embeddings/stackoverflow_mpnet-base')
text = "Replace me by any question / answer you'd like."
text_embbedding = model.encode(text)
# array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106,
# -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...],
# dtype=float32)
```
# Training procedure
## Pre-training
We use the pretrained [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base). Please refer to the model
card for more detailed information about the pre-training procedure.
## Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
### Hyper parameters
We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository.
### Training data
We used 18,562,443 (title, body) pairs from StackOverflow as training data.
| Dataset | Paper | Number of training tuples |
|:--------------------------------------------------------:|:----------------------------------------:|:--------------------------:|
| StackOverflow title body pairs | - | 18,562,443 |
|
flax-sentence-embeddings/multi-qa_v1-mpnet-mean_cos
|
flax-sentence-embeddings
| 2021-07-26T01:35:19Z | 10 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"arxiv:2102.07033",
"arxiv:2104.08727",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# multi-qa_v1-mpnet-mean_cos
## Model Description
SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used a pretrained [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) model and trained it using Siamese Network setup and contrastive learning objective. Question and answer pairs from StackExchange was used as training data to make the model robust to Question / Answer embedding similarity. For this model, mean pooling of hidden states were used as sentence embeddings.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well
as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks.
## Intended uses
Our model is intended to be used as a sentence encoder for a search engine. Given an input sentence, it outputs a vector which captures
the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks.
## How to use
Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('flax-sentence-embeddings/multi-qa_v1-mpnet-mean_cos')
text = "Replace me by any question / answer you'd like."
text_embbedding = model.encode(text)
# array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106,
# -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...],
# dtype=float32)
```
# Training procedure
## Pre-training
We use the pretrained [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base). Please refer to the model
card for more detailed information about the pre-training procedure.
## Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
### Hyper parameters
We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository.
### Training data
We used the concatenation from multiple Stackexchange Question-Answer datasets to fine-tune our model. MSMARCO, NQ & other question-answer datasets were also used.
| Dataset | Paper | Number of training tuples |
|:--------------------------------------------------------:|:----------------------------------------:|:--------------------------:|
| [Stack Exchange QA - Title & Answer](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) | - | 4,750,619 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) | - | 364,001 |
| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [PAQ](https://github.com/facebookresearch/PAQ) | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [MS MARCO](https://microsoft.github.io/msmarco/) | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| SearchQA | - | 582,261 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
|
flax-sentence-embeddings/multi-qa_v1-mpnet-cls_dot
|
flax-sentence-embeddings
| 2021-07-26T01:34:59Z | 8 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"arxiv:2102.07033",
"arxiv:2104.08727",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# multi-qa_v1-mpnet-cls_dot
## Model Description
SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used a pretrained [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) model and trained it using Siamese Network setup and contrastive learning objective. Question and answer pairs from StackExchange was used as training data to make the model robust to Question / Answer embedding similarity. For this model, cls output was used instead of mean pooling as sentence embeddings. Dot product was used to calculate similarity for learning objective.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well
as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks.
## Intended uses
Our model is intended to be used as a sentence encoder for a search engine. Given an input sentence, it outputs a vector which captures
the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks.
## How to use
Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('flax-sentence-embeddings/multi-qa_v1-mpnet-cls_dot')
text = "Replace me by any question / answer you'd like."
text_embbedding = model.encode(text)
# array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106,
# -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...],
# dtype=float32)
```
# Training procedure
## Pre-training
We use the pretrained [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base). Please refer to the model
card for more detailed information about the pre-training procedure.
## Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
### Hyper parameters
We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository.
### Training data
We used the concatenation from multiple Stackexchange Question-Answer datasets to fine-tune our model. MSMARCO, NQ & other question-answer datasets were also used.
| Dataset | Paper | Number of training tuples |
|:--------------------------------------------------------:|:----------------------------------------:|:--------------------------:|
| [Stack Exchange QA - Title & Answer](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) | - | 4,750,619 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) | - | 364,001 |
| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [PAQ](https://github.com/facebookresearch/PAQ) | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [MS MARCO](https://microsoft.github.io/msmarco/) | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| SearchQA | - | 582,261 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
|
flax-sentence-embeddings/multi-qa_v1-distilbert-mean_cos
|
flax-sentence-embeddings
| 2021-07-26T01:34:46Z | 155 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"arxiv:2102.07033",
"arxiv:2104.08727",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# multi-qa_v1-distilbert-mean_cos
## Model Description
SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used a pretrained [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) model and trained it using Siamese Network setup and contrastive learning objective. Question and answer pairs from StackExchange was used as training data to make the model robust to Question / Answer embedding similarity. For this model, mean pooling of hidden states were used as sentence embeddings.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well
as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks.
## Intended uses
Our model is intended to be used as a sentence encoder for a search engine. Given an input sentence, it outputs a vector which captures
the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks.
## How to use
Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('flax-sentence-embeddings/multi-qa_v1-distilbert-mean_cos')
text = "Replace me by any question / answer you'd like."
text_embbedding = model.encode(text)
# array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106,
# -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...],
# dtype=float32)
```
# Training procedure
## Pre-training
We use the pretrained [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased). Please refer to the model
card for more detailed information about the pre-training procedure.
## Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
### Hyper parameters
We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository.
### Training data
We used the concatenation from multiple Stackexchange Question-Answer datasets to fine-tune our model. MSMARCO, NQ & other question-answer datasets were also used.
| Dataset | Paper | Number of training tuples |
|:--------------------------------------------------------:|:----------------------------------------:|:--------------------------:|
| [Stack Exchange QA - Title & Answer](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) | - | 4,750,619 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) | - | 364,001 |
| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [PAQ](https://github.com/facebookresearch/PAQ) | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [MS MARCO](https://microsoft.github.io/msmarco/) | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| SearchQA | - | 582,261 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
|
flax-sentence-embeddings/multi-qa_v1-distilbert-cls_dot
|
flax-sentence-embeddings
| 2021-07-26T01:34:32Z | 165 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"arxiv:2102.07033",
"arxiv:2104.08727",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# multi-qa_v1-distilbert-cls_dot
## Model Description
SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used a pretrained [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) model and trained it using Siamese Network setup and contrastive learning objective. Question and answer pairs from StackExchange was used as training data to make the model robust to Question / Answer embedding similarity. For this model, cls output was used instead of mean pooling as sentence embeddings. Dot product was used to calculate similarity for learning objective.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well
as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks.
## Intended uses
Our model is intended to be used as a sentence encoder for a search engine. Given an input sentence, it outputs a vector which captures
the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks.
## How to use
Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('flax-sentence-embeddings/multi-qa_v1-distilbert-cls_dot')
text = "Replace me by any question / answer you'd like."
text_embbedding = model.encode(text)
# array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106,
# -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...],
# dtype=float32)
```
# Training procedure
## Pre-training
We use the pretrained [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased). Please refer to the model
card for more detailed information about the pre-training procedure.
## Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
### Hyper parameters
We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository.
### Training data
We used the concatenation from multiple Stackexchange Question-Answer datasets to fine-tune our model. MSMARCO, NQ & other question-answer datasets were also used.
| Dataset | Paper | Number of training tuples |
|:--------------------------------------------------------:|:----------------------------------------:|:--------------------------:|
| [Stack Exchange QA - Title & Answer](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) | - | 4,750,619 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) | - | 364,001 |
| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [PAQ](https://github.com/facebookresearch/PAQ) | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [MS MARCO](https://microsoft.github.io/msmarco/) | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| SearchQA | - | 582,261 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
|
flax-sentence-embeddings/multi-qa_v1-MiniLM-L6-mean_cos
|
flax-sentence-embeddings
| 2021-07-26T01:34:18Z | 13 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"arxiv:2102.07033",
"arxiv:2104.08727",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# multi-qa_v1-MiniLM-L6-mean_cos
## Model Description
SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used a pretrained [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and trained it using Siamese Network setup and contrastive learning objective. Question and answer pairs from StackExchange was used as training data to make the model robust to Question / Answer embedding similarity. For this model, mean pooling of the hidden states were used as sentence embeddings.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well
as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks.
## Intended uses
Our model is intended to be used as a sentence encoder for a search engine. Given an input sentence, it outputs a vector which captures
the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks.
## How to use
Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('flax-sentence-embeddings/multi-qa_v1-MiniLM-L6-mean_cos')
text = "Replace me by any question / answer you'd like."
text_embbedding = model.encode(text)
# array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106,
# -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...],
# dtype=float32)
```
# Training procedure
## Pre-training
We use the pretrained [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased). Please refer to the model
card for more detailed information about the pre-training procedure.
## Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
### Hyper parameters
We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository.
### Training data
We used the concatenation from multiple Stackexchange Question-Answer datasets to fine-tune our model. MSMARCO, NQ & other question-answer datasets were also used.
| Dataset | Paper | Number of training tuples |
|:--------------------------------------------------------:|:----------------------------------------:|:--------------------------:|
| [Stack Exchange QA - Title & Answer](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) | - | 4,750,619 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) | - | 364,001 |
| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [PAQ](https://github.com/facebookresearch/PAQ) | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [MS MARCO](https://microsoft.github.io/msmarco/) | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| SearchQA | - | 582,261 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
|
flax-sentence-embeddings/multi-qa_v1-MiniLM-L6-cls_dot
|
flax-sentence-embeddings
| 2021-07-26T01:33:36Z | 354 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"arxiv:2102.07033",
"arxiv:2104.08727",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# multi-qa_v1-MiniLM-L6-cls_dot
## Model Description
SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used a pretrained [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and trained it using Siamese Network setup and contrastive learning objective. Question and answer pairs from StackExchange was used as training data to make the model robust to Question / Answer embedding similarity. For this model, cls output was used instead of mean pooling as sentence embeddings. Dot product was used to calculate similarity for learning objective.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well
as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks.
## Intended uses
Our model is intended to be used as a sentence encoder for a search engine. Given an input sentence, it outputs a vector which captures
the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks.
## How to use
Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('flax-sentence-embeddings/multi-qa_v1-MiniLM-L6-cls_dot')
text = "Replace me by any question / answer you'd like."
text_embbedding = model.encode(text)
# array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106,
# -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...],
# dtype=float32)
```
# Training procedure
## Pre-training
We use the pretrained [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased). Please refer to the model
card for more detailed information about the pre-training procedure.
## Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
### Hyper parameters
We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository.
### Training data
We used the concatenation from multiple Stackexchange Question-Answer datasets to fine-tune our model. MSMARCO, NQ & other question-answer datasets were also used.
| Dataset | Paper | Number of training tuples |
|:--------------------------------------------------------:|:----------------------------------------:|:--------------------------:|
| [Stack Exchange QA - Title & Answer](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) | - | 4,750,619 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) | - | 364,001 |
| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [PAQ](https://github.com/facebookresearch/PAQ) | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [MS MARCO](https://microsoft.github.io/msmarco/) | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| SearchQA | - | 582,261 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
|
huggingtweets/thebaronskelly
|
huggingtweets
| 2021-07-25T22:46:45Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/thebaronskelly/1627253182833/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1412243136626188292/5XN3zStP_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Skelly</div>
<div style="text-align: center; font-size: 14px;">@thebaronskelly</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Skelly.
| Data | Skelly |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 298 |
| Short tweets | 1337 |
| Tweets kept | 1615 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4aab6en9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @thebaronskelly's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3lhgo3xu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3lhgo3xu/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/thebaronskelly')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/horse1350
|
huggingtweets
| 2021-07-25T22:17:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/horse1350/1627251450034/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1417892518981738502/Qb2SoLGO_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">horsey</div>
<div style="text-align: center; font-size: 14px;">@horse1350</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from horsey.
| Data | horsey |
| --- | --- |
| Tweets downloaded | 1783 |
| Retweets | 26 |
| Short tweets | 352 |
| Tweets kept | 1405 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2gpy0wuu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @horse1350's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fgwh8u1y) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fgwh8u1y/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/horse1350')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/aimbotaimy-ladydarknest
|
huggingtweets
| 2021-07-25T20:33:04Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/aimbotaimy-ladydarknest/1627245180529/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374872808136835072/hPahIg-A_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1409725677495009283/RPVDIGan_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">AimbotAimy 🍞🔞 NSFW V-Tuber & Demon Lord Yeefi NSFW🔞</div>
<div style="text-align: center; font-size: 14px;">@aimbotaimy-ladydarknest</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from AimbotAimy 🍞🔞 NSFW V-Tuber & Demon Lord Yeefi NSFW🔞.
| Data | AimbotAimy 🍞🔞 NSFW V-Tuber | Demon Lord Yeefi NSFW🔞 |
| --- | --- | --- |
| Tweets downloaded | 528 | 3242 |
| Retweets | 61 | 957 |
| Short tweets | 130 | 392 |
| Tweets kept | 337 | 1893 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/uz56dprc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @aimbotaimy-ladydarknest's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1di7czlx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1di7czlx/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/aimbotaimy-ladydarknest')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/aimbotaimy-demi_naga-livingscribe
|
huggingtweets
| 2021-07-25T18:00:56Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/aimbotaimy-demi_naga-livingscribe/1627235967135/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374872808136835072/hPahIg-A_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1405364006475296773/0i4RCEH5_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1408966863804063749/fTuaNcZ__400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">AimbotAimy 🍞🔞 NSFW V-Tuber & Poe's Law 🇷🇺: 3.33 You can (not) redo & Demi 'ドヤ顔' Naga</div>
<div style="text-align: center; font-size: 14px;">@aimbotaimy-demi_naga-livingscribe</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from AimbotAimy 🍞🔞 NSFW V-Tuber & Poe's Law 🇷🇺: 3.33 You can (not) redo & Demi 'ドヤ顔' Naga.
| Data | AimbotAimy 🍞🔞 NSFW V-Tuber | Poe's Law 🇷🇺: 3.33 You can (not) redo | Demi 'ドヤ顔' Naga |
| --- | --- | --- | --- |
| Tweets downloaded | 497 | 3242 | 3234 |
| Retweets | 60 | 433 | 909 |
| Short tweets | 125 | 564 | 341 |
| Tweets kept | 312 | 2245 | 1984 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/32v27r5o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @aimbotaimy-demi_naga-livingscribe's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2qs4c0sr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2qs4c0sr/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/aimbotaimy-demi_naga-livingscribe')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/dead__bug
|
huggingtweets
| 2021-07-25T16:53:13Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/dead__bug/1627231954071/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1349980097168740352/GSthZg8p_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">persona non greta</div>
<div style="text-align: center; font-size: 14px;">@dead__bug</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from persona non greta.
| Data | persona non greta |
| --- | --- |
| Tweets downloaded | 3095 |
| Retweets | 449 |
| Short tweets | 623 |
| Tweets kept | 2023 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/oyzjw1jc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dead__bug's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/20dghuyx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/20dghuyx/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/dead__bug')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Alireza1044/albert-base-v2-cola
|
Alireza1044
| 2021-07-25T16:25:10Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model_index:
- name: cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metric:
name: Matthews Correlation
type: matthews_correlation
value: 0.5494768667363472
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cola
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7552
- Matthews Correlation: 0.5495
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
flax-community/gpt2-swahili
|
flax-community
| 2021-07-25T16:22:24Z | 19 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"sw",
"dataset:flax-community/swahili-safi",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: sw
widget:
- text: "Ninitaka kukula"
datasets:
- flax-community/swahili-safi
---
## GPT2 in Swahili
This model was trained using HuggingFace's Flax framework and is part of the [JAX/Flax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organized by [HuggingFace](https://huggingface.co). All training was done on a TPUv3-8 VM sponsored by the Google Cloud team.
## How to use
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt2-swahili")
model = AutoModelWithLMHead.from_pretrained("flax-community/gpt2-swahili")
print(round((model.num_parameters())/(1000*1000)),"Million Parameters")
124 Million Parameters
```
#### **Training Data**:
This model was trained on [Swahili Safi](https://huggingface.co/datasets/flax-community/swahili-safi)
#### **More Details**:
For more details and Demo please check [HF Swahili Space](https://huggingface.co/spaces/flax-community/Swahili)
|
flax-community/roberta-swahili
|
flax-community
| 2021-07-25T16:21:02Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"sw",
"dataset:flax-community/swahili-safi",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: sw
widget:
- text: "Si kila mwenye makucha <mask> simba."
datasets:
- flax-community/swahili-safi
---
## RoBERTa in Swahili
This model was trained using HuggingFace's Flax framework and is part of the [JAX/Flax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organized by [HuggingFace](https://huggingface.co). All training was done on a TPUv3-8 VM sponsored by the Google Cloud team.
## How to use
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("flax-community/roberta-swahili")
model = AutoModelForMaskedLM.from_pretrained("flax-community/roberta-swahili")
print(round((model.num_parameters())/(1000*1000)),"Million Parameters")
105 Million Parameters
```
#### **Training Data**:
This model was trained on [Swahili Safi](https://huggingface.co/datasets/flax-community/swahili-safi)
#### **Results**:
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) [](https://colab.research.google.com/drive/1OIurb4J91X7461NQXLCCGzjeEGJq_Tyl?usp=sharing)
```
Eval metrics: {'f1': 86%}
```
This [model](https://huggingface.co/flax-community/roberta-swahili-news-classification) was fine-tuned based off this model for the
[Zindi News Classification Challenge](https://zindi.africa/hackathons/ai4d-swahili-news-classification-challenge)
#### **More Details**:
For more details and Demo please check [HF Swahili Space](https://huggingface.co/spaces/flax-community/Swahili)
|
flax-community/bert-base-uncased-swahili
|
flax-community
| 2021-07-25T16:19:20Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"bert",
"fill-mask",
"sw",
"dataset:flax-community/swahili-safi",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: sw
widget:
- text: "Si kila mwenye makucha [MASK] simba."
datasets:
- flax-community/swahili-safi
---
## BERT base-uncased for in Swahili
This model was trained using HuggingFace's Flax framework and is part of the [JAX/Flax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organized by [HuggingFace](https://huggingface.co). All training was done on a TPUv3-8 VM sponsored by the Google Cloud team.
## How to use
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("flax-community/bert-base-uncased-swahili")
model = AutoModelForMaskedLM.from_pretrained("flax-community/bert-base-uncased-swahili")
print(round((model.num_parameters())/(1000*1000)),"Million Parameters")
110 Million Parameters
```
#### **Training Data**:
This model was trained on [Swahili Safi](https://huggingface.co/datasets/flax-community/swahili-safi)
#### **More Details**:
For more details and Demo please check [HF Swahili Space](https://huggingface.co/spaces/flax-community/Swahili)
|
flax-community/vqgan_f16_16384
|
flax-community
| 2021-07-25T15:15:36Z | 11 | 9 |
transformers
|
[
"transformers",
"jax",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
## VQGAN-f16-16384
### Model Description
This is a Flax/JAX implementation of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in [Taming Transformers for High-Resolution Image Synthesis](https://compvis.github.io/taming-transformers/) ([CVPR paper](https://openaccess.thecvf.com/content/CVPR2021/html/Esser_Taming_Transformers_for_High-Resolution_Image_Synthesis_CVPR_2021_paper.html)).
The model allows the encoding of images as a fixed-length sequence of tokens taken from the codebook.
This version of the model uses a reduction factor `f=16` and a vocabulary of `13,384` tokens.
As an example of how the reduction factor works, images of size `256x256` are encoded to sequences of `256` tokens: `256/16 * 256/16`. Images of `512x512` would result in sequences of `1024` tokens.
### Datasets Used for Training
* ImageNet. We didn't train this model from scratch. Instead, we started from [a checkpoint pre-trained on ImageNet](https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/).
* [Conceptual Captions 3M](https://ai.google.com/research/ConceptualCaptions/) (CC3M).
* [OpenAI subset of YFCC100M](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md).
We fine-tuned on CC3M and YFCC100M to improve the encoding quality of people and faces, which are not very well represented in ImageNet. We used a subset of 2,268,720 images from CC3M and YFCC100M for this purpose.
### Training Process
Finetuning was performed in PyTorch using [taming-transformers](https://github.com/CompVis/taming-transformers). The full training process and model preparation includes these steps:
* Pre-training on ImageNet. Previously performed. We used [this checkpoint](https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887).
* Fine-tuning, [Part 1](https://wandb.ai/wandb/hf-flax-dalle-mini/runs/2021-07-09T15-33-11_dalle_vqgan?workspace=user-borisd13).
* Fine-tuning, [Part 2](https://wandb.ai/wandb/hf-flax-dalle-mini/runs/2021-07-09T21-42-07_dalle_vqgan?workspace=user-borisd13) – continuation from Part 1. The final checkpoint was uploaded to [boris/vqgan_f16_16384](https://huggingface.co/boris/vqgan_f16_16384).
* Conversion to JAX, which is the model described in this card.
### How to Use
The checkpoint can be loaded using [Suraj Patil's implementation](https://github.com/patil-suraj/vqgan-jax) of `VQModel`.
* Example notebook, heavily based in work by [Suraj](https://huggingface.co/valhalla): [](https://colab.research.google.com/github/borisdayma/dalle-mini/blob/main/dev/vqgan/JAX_VQGAN_f16_16384_Reconstruction.ipynb)
* Batch encoding using JAX `pmap`, complete example including data loading with PyTorch:
```python
# VQGAN-JAX - pmap encoding HowTo
import numpy as np
# For data loading
import torch
import torchvision.transforms.functional as TF
from torch.utils.data import Dataset, DataLoader
from torchvision.datasets.folder import default_loader
from torchvision.transforms import InterpolationMode
# For data saving
from pathlib import Path
import pandas as pd
from tqdm import tqdm
import jax
from jax import pmap
from vqgan_jax.modeling_flax_vqgan import VQModel
## Params and arguments
# List of paths containing images to encode
image_list = '/sddata/dalle-mini/CC12M/10k.tsv'
output_tsv = 'output.tsv' # Encoded results
batch_size = 64
num_workers = 4 # TPU v3-8s have 96 cores, so feel free to increase this number when necessary
# Load model
model = VQModel.from_pretrained("flax-community/vqgan_f16_16384")
## Data Loading.
# Simple torch Dataset to load images from paths.
# You can use your own pipeline instead.
class ImageDataset(Dataset):
def __init__(self, image_list_path: str, image_size: int, max_items=None):
"""
:param image_list_path: Path to a file containing a list of all images. We assume absolute paths for now.
:param image_size: Image size. Source images will be resized and center-cropped.
:max_items: Limit dataset size for debugging
"""
self.image_list = pd.read_csv(image_list_path, sep='\t', header=None)
if max_items is not None: self.image_list = self.image_list[:max_items]
self.image_size = image_size
def __len__(self):
return len(self.image_list)
def _get_raw_image(self, i):
image_path = Path(self.image_list.iloc[i][0])
return default_loader(image_path)
def resize_image(self, image):
s = min(image.size)
r = self.image_size / s
s = (round(r * image.size[1]), round(r * image.size[0]))
image = TF.resize(image, s, interpolation=InterpolationMode.LANCZOS)
image = TF.center_crop(image, output_size = 2 * [self.image_size])
image = np.expand_dims(np.array(image), axis=0)
return image
def __getitem__(self, i):
image = self._get_raw_image(i)
return self.resize_image(image)
## Encoding
# Encoding function to be parallelized with `pmap`
# Note: images have to be square
def encode(model, batch):
_, indices = model.encode(batch)
return indices
# Alternative: create a batch with num_tpus*batch_size and use `shard` to distribute.
def superbatch_generator(dataloader, num_tpus):
iter_loader = iter(dataloader)
for batch in iter_loader:
superbatch = [batch.squeeze(1)]
try:
for _ in range(num_tpus-1):
batch = next(iter_loader)
if batch is None:
break
# Skip incomplete last batch
if batch.shape[0] == dataloader.batch_size:
superbatch.append(batch.squeeze(1))
except StopIteration:
pass
superbatch = torch.stack(superbatch, axis=0)
yield superbatch
def encode_dataset(dataset, batch_size=32):
dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)
superbatches = superbatch_generator(dataloader, num_tpus=jax.device_count())
num_tpus = jax.device_count()
dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)
superbatches = superbatch_generator(dataloader, num_tpus=num_tpus)
p_encoder = pmap(lambda batch: encode(model, batch))
# Save each superbatch to avoid reallocation of buffers as we process them.
# Keep the file open to prevent excessive file seeks.
with open(output_tsv, "w") as file:
iterations = len(dataset) // (batch_size * num_tpus)
for n in tqdm(range(iterations)):
superbatch = next(superbatches)
encoded = p_encoder(superbatch.numpy())
encoded = encoded.reshape(-1, encoded.shape[-1])
# Extract paths from the dataset, save paths and encodings (as string)
start_index = n * batch_size * num_tpus
end_index = (n+1) * batch_size * num_tpus
paths = dataset.image_list[start_index:end_index][0].values
encoded_as_string = list(map(lambda item: np.array2string(item, separator=',', max_line_width=50000, formatter={'int':lambda x: str(x)}), encoded))
batch_df = pd.DataFrame.from_dict({"image_file": paths, "encoding": encoded_as_string})
batch_df.to_csv(file, sep='\t', header=(n==0), index=None)
dataset = ImageDataset(image_list, image_size=256)
encoded_dataset = encode_dataset(dataset, batch_size=batch_size)
```
### Related Models in the Hub
* PyTorch version of VQGAN, trained on the same datasets described here: [boris/vqgan_f16_16384](https://huggingface.co/boris/vqgan_f16_16384).
* [DALL·E mini](https://huggingface.co/flax-community/dalle-mini), a Flax/JAX simplified implementation of OpenAI's DALL·E.
### Other
This model was successfully used as part of the implementation of [DALL·E mini](https://github.com/borisdayma/dalle-mini). Our [report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA) contains more details on how to leverage it in an image encoding / generation pipeline.
|
andi611/distilbert-base-uncased-squad2-with-ner
|
andi611
| 2021-07-25T14:29:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- conll2003
model_index:
- name: distilbert-base-uncased-squad2-with-ner
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: conll2003
type: conll2003
args: conll2003
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-squad2-with-ner
This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
cristinae/marian_caes2en
|
cristinae
| 2021-07-25T10:24:27Z | 0 | 0 | null |
[
"translation",
"ca",
"es",
"en",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
language:
- ca
- es
- en
tags:
- translation
---
### Preprocessing
1. Normalisation and tokenisation with moses scripts
2. truecased with model docgWP.tcmodel.[LAN] and moses scripts
3. bped with model model.caesen40k.bpe and subword-nmt
- Note: no prepended tag for multilinguality
### Training Data
1. Bilingual es-ca: DOGC, Wikimatrix, OpenSubtitles, JW300, GlobalVoices
* Bilingual es-ca: Translations using systems trained with 1. of Oscar and Wikipedia
2. Bilingual es-en, ca-en: United Nations, Europarl, Wikimatrix, OpenSubtitles, JW300
* Bilingual es-en, ca-en: Translations using systems trained with 1. of the missing pairs
- Final training data size for the ca/es-en: 44M parallel sentences
- Finetuned with 1.5M real parallel data (without backtranslations)
### Model
Transformer big with guided alignments. Relevant parameters:
--beam-size 6
--normalize 0.6
--enc-depth 6 --dec-depth 6 --transformer-heads 8
--transformer-preprocess n --transformer-postprocess da
--transformer-dropout 0.1
--label-smoothing 0.1
--dim-emb 1024 --transformer-dim-ffn 4096
--transformer-dropout-attention 0.1
--transformer-dropout-ffn 0.1
--learn-rate 0.00015 --lr-warmup 8000 --lr-decay-inv-sqrt 8000
--optimizer-params 0.9 0.998 1e-09
--clip-norm 5
--tied-embeddings
--exponential-smoothing
--transformer-guided-alignment-layer 1 --guided-alignment-cost mse --guided-alignment-weight 0.1
## Evaluation
### Test set
https://github.com/PLXIV/Gebiotoolkit/tree/master/gebiocorpus_v2
### ca2en
BLEU|#:1|bs:1000|rs:12345|c:mixed|e:no|tok:13a|s:exp|v:2.0.0 = 47.8 (μ = 47.8 ± 0.9)
chrF|#:1|bs:1000|rs:12345|c:mixed|e:yes|nc:6|nw:0|s:no|v:2.0.0 = 69.9 (μ = 69.9 ± 0.7)
### es2en
BLEU|#:1|bs:1000|rs:12345|c:mixed|e:no|tok:13a|s:exp|v:2.0.0 = 48.9 (μ = 48.9 ± 0.9)
chrF2|#:1|bs:1000|rs:12345|c:mixed|e:yes|nc:6|nw:0|s:no|v:2.0.0 = 70.5 (μ = 70.5 ± 0.7)
|
vivekRahul/animal_classifier_huggingface
|
vivekRahul
| 2021-07-25T06:02:38Z | 88 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: animal_classifier_huggingface
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9910714030265808
---
# animal_classifier_huggingface
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### cat
#### dog
#### elephant
#### lion
#### tiger
|
huggingtweets/clamtime-daramgaria-lazar181
|
huggingtweets
| 2021-07-25T04:12:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/clamtime-daramgaria-lazar181/1627186361489/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1387170139599212547/6jVRvWgF_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1408716131867713538/rg3HSZ5D_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1409230363906424832/67a8m2BA_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ari @ 😴 & clementine!!!! 𓃠 & Ho3K | Daramgar 🔜 CROSSxUP</div>
<div style="text-align: center; font-size: 14px;">@clamtime-daramgaria-lazar181</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Ari @ 😴 & clementine!!!! 𓃠 & Ho3K | Daramgar 🔜 CROSSxUP.
| Data | Ari @ 😴 | clementine!!!! 𓃠 | Ho3K | Daramgar 🔜 CROSSxUP |
| --- | --- | --- | --- |
| Tweets downloaded | 3232 | 3185 | 3249 |
| Retweets | 512 | 438 | 30 |
| Short tweets | 590 | 720 | 805 |
| Tweets kept | 2130 | 2027 | 2414 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/397xumbr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @clamtime-daramgaria-lazar181's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/37plk0db) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/37plk0db/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/clamtime-daramgaria-lazar181')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/aimbotaimy
|
huggingtweets
| 2021-07-25T03:52:26Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/aimbotaimy/1627185142630/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374872808136835072/hPahIg-A_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">AimbotAimy 🍞🔞 NSFW V-Tuber</div>
<div style="text-align: center; font-size: 14px;">@aimbotaimy</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from AimbotAimy 🍞🔞 NSFW V-Tuber.
| Data | AimbotAimy 🍞🔞 NSFW V-Tuber |
| --- | --- |
| Tweets downloaded | 491 |
| Retweets | 59 |
| Short tweets | 125 |
| Tweets kept | 307 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/38rsh6x7/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @aimbotaimy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2sn41u12) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2sn41u12/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/aimbotaimy')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/ridingthescree
|
huggingtweets
| 2021-07-24T23:45:08Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/ridingthescree/1627170304459/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1365877163652640768/1i1yvZlT_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Riding the Scree</div>
<div style="text-align: center; font-size: 14px;">@ridingthescree</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Riding the Scree.
| Data | Riding the Scree |
| --- | --- |
| Tweets downloaded | 1813 |
| Retweets | 410 |
| Short tweets | 32 |
| Tweets kept | 1371 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/22bfjscr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ridingthescree's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2p5u6rux) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2p5u6rux/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ridingthescree')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/celosia2
|
huggingtweets
| 2021-07-24T17:58:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/celosia2/1627149452177/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1251490479990022145/lS6i5Wgy_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Celosia2 🌻 Kristi 💚</div>
<div style="text-align: center; font-size: 14px;">@celosia2</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Celosia2 🌻 Kristi 💚.
| Data | Celosia2 🌻 Kristi 💚 |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 613 |
| Short tweets | 494 |
| Tweets kept | 2140 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ohtfdalm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @celosia2's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/xzr0nuzp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/xzr0nuzp/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/celosia2')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/sshakestation
|
huggingtweets
| 2021-07-24T17:44:37Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/sshakestation/1627148673612/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1390378853877510145/YdbZXqjN_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">RJ's Shake Station</div>
<div style="text-align: center; font-size: 14px;">@sshakestation</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from RJ's Shake Station.
| Data | RJ's Shake Station |
| --- | --- |
| Tweets downloaded | 456 |
| Retweets | 10 |
| Short tweets | 28 |
| Tweets kept | 418 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wszsjtre/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @sshakestation's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3k91nzds) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3k91nzds/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/sshakestation')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/lauradmcbryde
|
huggingtweets
| 2021-07-24T15:03:09Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/lauradmcbryde/1627138961068/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1384965601492353026/KlIO_YsH_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Laura D McBryde</div>
<div style="text-align: center; font-size: 14px;">@lauradmcbryde</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Laura D McBryde.
| Data | Laura D McBryde |
| --- | --- |
| Tweets downloaded | 3233 |
| Retweets | 205 |
| Short tweets | 453 |
| Tweets kept | 2575 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ry0eljz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lauradmcbryde's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/g2wyxs4u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/g2wyxs4u/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/lauradmcbryde')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
sultan/BioM-ELECTRA-Base-SQuAD2-BioASQ8B
|
sultan
| 2021-07-24T14:43:28Z | 36 | 1 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer models
using different design choices. We evaluate
the performance of our pretrained models
against other existing biomedical language
models in the literature. Our results show that
we achieve state-of-the-art results on several
biomedical domain tasks despite using similar
or less computational cost compared to other
models in the literature. Our findings highlight
the significant effect of design choices on
improving the performance of biomedical language
models.
# Model Description
- This model is fine-tuned on the SQuAD2.0 dataset and then on the BioASQ8B-Factoid training dataset. We convert the BioASQ8B-Factoid training dataset to SQuAD1.1 format and train and evaluate our model (BioM-ELECTRA-Base-SQuAD2) on this dataset.
- You can use this model to make a prediction (inference) directly without fine-tuning it. Try to enter a PubMed abstract in the context box in this model card and try out a couple of biomedical questions within the given context and see how it performs compared to ELECTRA original model. This model should also be useful for creating a pandemic QA system (e.g., COVID-19) .
- Please note that this version (PyTorch) is different than what we used in our participation in BioASQ9B (TensorFlow with Layer-Wise Decay). We combine all five batches of the BioASQ8B testing dataset as one dev.json file.
- Below is unofficial results of our models against the original ELECTRA base and large :
| Model | Exact Match (EM) | F1 Score |
| --- | --- | --- |
| ELECTRA-Base-SQuAD2-BioASQ8B | 61.89 | 74.39 |
| **BioM-ELECTRA-Base-SQuAD2-BioASQ8B** | **70.31** | **80.90** |
| ELECTRA-Large-SQuAD2-BioASQ8B | 67.36 | 78.90 |
| BioM-ELECTRA-Large-SQuAD2-BioASQ8B | 74.31 | 84.72 |
Training script
```python
python3 run_squad.py --model_type electra --model_name_or_path sultan/BioM-ELECTRA-Base-SQuAD2 \
--train_file BioASQ8B/train.json \
--predict_file BioASQ8B/dev.json \
--do_lower_case \
--do_train \
--do_eval \
--threads 20 \
--version_2_with_negative \
--num_train_epochs 3 \
--learning_rate 3e-5 \
--max_seq_length 512 \
--doc_stride 128 \
--per_gpu_train_batch_size 8 \
--gradient_accumulation_steps 2 \
--per_gpu_eval_batch_size 128 \
--logging_steps 50 \
--save_steps 5000 \
--fp16 \
--fp16_opt_level O1 \
--overwrite_output_dir \
--output_dir BioM-ELECTRA-Base-SQuAD-BioASQ \
--overwrite_cache
```
# Acknowledgment
We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units.
# Citation
```bibtex
@inproceedings{alrowili-shanker-2021-biom,
title = "{B}io{M}-Transformers: Building Large Biomedical Language Models with {BERT}, {ALBERT} and {ELECTRA}",
author = "Alrowili, Sultan and
Shanker, Vijay",
booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bionlp-1.24",
pages = "221--227",
abstract = "The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models.",
}
```
|
huggingtweets/poss_em
|
huggingtweets
| 2021-07-24T05:28:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1416065098745999364/LaFosSZA_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Poss'em</div>
<div style="text-align: center; font-size: 14px;">@poss_em</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Poss'em.
| Data | Poss'em |
| --- | --- |
| Tweets downloaded | 245 |
| Retweets | 26 |
| Short tweets | 19 |
| Tweets kept | 200 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/fn04icv3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @poss_em's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1m889m52) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1m889m52/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/poss_em')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/dril-jdogmart-redfieldcooper
|
huggingtweets
| 2021-07-24T02:22:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/dril-jdogmart-redfieldcooper/1627093373715/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1363680905215291399/Bl--YnLP_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1418244914597486594/nDL8WsU2_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wint & Jan Dogmart & Ronnie</div>
<div style="text-align: center; font-size: 14px;">@dril-jdogmart-redfieldcooper</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from wint & Jan Dogmart & Ronnie.
| Data | wint | Jan Dogmart | Ronnie |
| --- | --- | --- | --- |
| Tweets downloaded | 3229 | 1339 | 3238 |
| Retweets | 464 | 107 | 586 |
| Short tweets | 311 | 245 | 378 |
| Tweets kept | 2454 | 987 | 2274 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ma9es8d/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dril-jdogmart-redfieldcooper's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/acu5gl39) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/acu5gl39/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/dril-jdogmart-redfieldcooper')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/c0up
|
huggingtweets
| 2021-07-24T01:26:20Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/c0up/1627089976491/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1209626102/c0up_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Abhilash</div>
<div style="text-align: center; font-size: 14px;">@c0up</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Abhilash.
| Data | Abhilash |
| --- | --- |
| Tweets downloaded | 3203 |
| Retweets | 1476 |
| Short tweets | 384 |
| Tweets kept | 1343 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1le73jjg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @c0up's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1tebog4r) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1tebog4r/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/c0up')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/nipsithesciguy
|
huggingtweets
| 2021-07-24T00:27:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/nipsithesciguy/1627086421551/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/994672771887230976/YNh3gRcP_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">🎄Bowman ⚡🎄🧬</div>
<div style="text-align: center; font-size: 14px;">@nipsithesciguy</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from 🎄Bowman ⚡🎄🧬.
| Data | 🎄Bowman ⚡🎄🧬 |
| --- | --- |
| Tweets downloaded | 3237 |
| Retweets | 576 |
| Short tweets | 309 |
| Tweets kept | 2352 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/32ni4c07/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nipsithesciguy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2xr16jbo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2xr16jbo/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/nipsithesciguy')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/staidindoors
|
huggingtweets
| 2021-07-23T23:26:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/staidindoors/1627082764759/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1418465930456092672/-iGnfQyn_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">staid</div>
<div style="text-align: center; font-size: 14px;">@staidindoors</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from staid.
| Data | staid |
| --- | --- |
| Tweets downloaded | 3240 |
| Retweets | 919 |
| Short tweets | 611 |
| Tweets kept | 1710 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1crkj9xo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @staidindoors's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/it5qlwh5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/it5qlwh5/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/staidindoors')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/smolserabean
|
huggingtweets
| 2021-07-23T22:52:13Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/smolserabean/1627080715021/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1406727363522666497/86n4KIIJ_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ari but awesome</div>
<div style="text-align: center; font-size: 14px;">@smolserabean</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Ari but awesome.
| Data | Ari but awesome |
| --- | --- |
| Tweets downloaded | 398 |
| Retweets | 150 |
| Short tweets | 70 |
| Tweets kept | 178 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1tas8okv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @smolserabean's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3afn50i3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3afn50i3/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/smolserabean')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/charmin-claireredacted
|
huggingtweets
| 2021-07-23T22:44:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/charmin-claireredacted/1627080262136/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/594202303025950720/8gB7TYkC_400x400.png')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/984455379659575296/-0punyb9_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Charmin & Claire</div>
<div style="text-align: center; font-size: 14px;">@charmin-claireredacted</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Charmin & Claire.
| Data | Charmin | Claire |
| --- | --- | --- |
| Tweets downloaded | 3250 | 3241 |
| Retweets | 22 | 523 |
| Short tweets | 129 | 627 |
| Tweets kept | 3099 | 2091 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/rtv7eufi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @charmin-claireredacted's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1rw1se40) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1rw1se40/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/charmin-claireredacted')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/clamtime-daramgaria-ledgeguard
|
huggingtweets
| 2021-07-23T22:19:47Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1409230363906424832/67a8m2BA_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1408716131867713538/rg3HSZ5D_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1415805087868391427/r5M55HF9_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ho3K | Daramgar 🔜 CROSSxUP & clementine!!!! 𓃠 & camera! (low tier)</div>
<div style="text-align: center; font-size: 14px;">@clamtime-daramgaria-ledgeguard</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Ho3K | Daramgar 🔜 CROSSxUP & clementine!!!! 𓃠 & camera! (low tier).
| Data | Ho3K | Daramgar 🔜 CROSSxUP | clementine!!!! 𓃠 | camera! (low tier) |
| --- | --- | --- | --- |
| Tweets downloaded | 3249 | 3185 | 3211 |
| Retweets | 30 | 439 | 1053 |
| Short tweets | 807 | 719 | 556 |
| Tweets kept | 2412 | 2027 | 1602 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2z4hkysf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @clamtime-daramgaria-ledgeguard's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2viwbf33) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2viwbf33/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/clamtime-daramgaria-ledgeguard')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/jimlbsp
|
huggingtweets
| 2021-07-23T22:17:03Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1418336753170239493/xADOlUfY_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Jimmy</div>
<div style="text-align: center; font-size: 14px;">@jimlbsp</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Jimmy.
| Data | Jimmy |
| --- | --- |
| Tweets downloaded | 3239 |
| Retweets | 348 |
| Short tweets | 383 |
| Tweets kept | 2508 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/213jvoqo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jimlbsp's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2rtm7jla) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2rtm7jla/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jimlbsp')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/daramgaria
|
huggingtweets
| 2021-07-23T22:04:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1409230363906424832/67a8m2BA_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ho3K | Daramgar 🔜 CROSSxUP</div>
<div style="text-align: center; font-size: 14px;">@daramgaria</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Ho3K | Daramgar 🔜 CROSSxUP.
| Data | Ho3K | Daramgar 🔜 CROSSxUP |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 30 |
| Short tweets | 807 |
| Tweets kept | 2412 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/z2deo4d4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @daramgaria's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/29cuxcz9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/29cuxcz9/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/daramgaria')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/spamemcspam
|
huggingtweets
| 2021-07-23T20:59:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/spamemcspam/1627073948338/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1362892272342196224/RSTBJB08_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Yes, I know my cat is ugly.</div>
<div style="text-align: center; font-size: 14px;">@spamemcspam</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Yes, I know my cat is ugly..
| Data | Yes, I know my cat is ugly. |
| --- | --- |
| Tweets downloaded | 3214 |
| Retweets | 977 |
| Short tweets | 228 |
| Tweets kept | 2009 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3mn5cki9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @spamemcspam's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1v7cmihj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1v7cmihj/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/spamemcspam')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/nickfehr
|
huggingtweets
| 2021-07-23T20:33:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/nickfehr/1627072418558/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1344063446724165632/vPhmdiXn_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">🥸</div>
<div style="text-align: center; font-size: 14px;">@nickfehr</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from 🥸.
| Data | 🥸 |
| --- | --- |
| Tweets downloaded | 3213 |
| Retweets | 1257 |
| Short tweets | 318 |
| Tweets kept | 1638 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/5qnyjo9k/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nickfehr's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/hq8xwgey) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/hq8xwgey/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/nickfehr')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/timheadadvocate
|
huggingtweets
| 2021-07-23T20:31:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/timheadadvocate/1627072155971/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1407718876922597380/z_7yVM7Q_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Thomas, the pro-bono Tim-Head Advocate ©️™️</div>
<div style="text-align: center; font-size: 14px;">@timheadadvocate</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Thomas, the pro-bono Tim-Head Advocate ©️™️.
| Data | Thomas, the pro-bono Tim-Head Advocate ©️™️ |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 81 |
| Short tweets | 571 |
| Tweets kept | 2595 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2zfgmuno/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @timheadadvocate's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3he0jj1j) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3he0jj1j/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/timheadadvocate')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/maddiebirds
|
huggingtweets
| 2021-07-23T19:12:50Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/maddiebirds/1627067546895/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1417269904638681088/-hPhZh_I_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Official Office Hours Mascot</div>
<div style="text-align: center; font-size: 14px;">@maddiebirds</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Official Office Hours Mascot.
| Data | Official Office Hours Mascot |
| --- | --- |
| Tweets downloaded | 3164 |
| Retweets | 925 |
| Short tweets | 505 |
| Tweets kept | 1734 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2nvjfva5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @maddiebirds's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3m5l77r1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3m5l77r1/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/maddiebirds')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/jdogmart
|
huggingtweets
| 2021-07-23T18:42:30Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/jdogmart/1627065726745/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1363680905215291399/Bl--YnLP_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Jan Dogmart</div>
<div style="text-align: center; font-size: 14px;">@jdogmart</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Jan Dogmart.
| Data | Jan Dogmart |
| --- | --- |
| Tweets downloaded | 1333 |
| Retweets | 106 |
| Short tweets | 243 |
| Tweets kept | 984 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/8hacy1dt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jdogmart's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/uebjr2z5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/uebjr2z5/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jdogmart')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/elizamuffins
|
huggingtweets
| 2021-07-23T18:02:59Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/elizamuffins/1627063374286/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/819298508545126401/KR63pu1p_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Junior Movie Buff</div>
<div style="text-align: center; font-size: 14px;">@elizamuffins</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Junior Movie Buff.
| Data | Junior Movie Buff |
| --- | --- |
| Tweets downloaded | 3225 |
| Retweets | 290 |
| Short tweets | 295 |
| Tweets kept | 2640 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3jfflcwa/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elizamuffins's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3bixjnvi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3bixjnvi/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/elizamuffins')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/evan_pincus
|
huggingtweets
| 2021-07-23T17:51:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/evan_pincus/1627062659543/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1305274903730421760/DhfkgCnC_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Jay-Z Ballard</div>
<div style="text-align: center; font-size: 14px;">@evan_pincus</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Jay-Z Ballard.
| Data | Jay-Z Ballard |
| --- | --- |
| Tweets downloaded | 3215 |
| Retweets | 608 |
| Short tweets | 342 |
| Tweets kept | 2265 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/yzmzh54y/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @evan_pincus's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/25ge5u94) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/25ge5u94/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/evan_pincus')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/herialc
|
huggingtweets
| 2021-07-23T17:32:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1233052680190296064/zcbLKhOR_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Claire</div>
<div style="text-align: center; font-size: 14px;">@herialc</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Claire.
| Data | Claire |
| --- | --- |
| Tweets downloaded | 539 |
| Retweets | 219 |
| Short tweets | 27 |
| Tweets kept | 293 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bop9va7/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @herialc's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/10twdkn3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/10twdkn3/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/herialc')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/ronnienumber7
|
huggingtweets
| 2021-07-23T17:28:08Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/ronnienumber7/1627061284718/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1418253489373913092/Wrsj1ZEj_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ronnie (the original Ronnie)</div>
<div style="text-align: center; font-size: 14px;">@ronnienumber7</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Ronnie (the original Ronnie).
| Data | Ronnie (the original Ronnie) |
| --- | --- |
| Tweets downloaded | 559 |
| Retweets | 11 |
| Short tweets | 35 |
| Tweets kept | 513 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/z46j8b3c/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ronnienumber7's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1n9lz23f) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1n9lz23f/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ronnienumber7')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Subsets and Splits
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