## license: apache-2.0


[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.


The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.pdf)'s salient span masking objective on [Wikipedia](https://huggingface.co/datasets/wikipedia), and finally fine-tuned on [Natural Questions (NQ)](https://huggingface.co/datasets/natural_questions).


**Note**: The model was fine-tuned on 90% of the train splits of [Natural Questions (NQ)](https://huggingface.co/datasets/natural_questions) for 20k steps and validated on the held-out 10% of the train split.


Other community Checkpoints: [here](https://huggingface.co/models?search=ssm)


Paper: [How Much Knowledge Can You Pack
Into the Parameters of a Language Model?](https://arxiv.org/abs/1910.10683.pdf)


Authors: *Adam Roberts, Colin Raffel, Noam Shazeer*


## Results on Natural Questions - Test Set


|Id | link | Exact Match  |
|---|---|---|
|**T5-large**|**https://huggingface.co/google/t5-large-ssm-nqo**|**29.0**|
|T5-xxl|https://huggingface.co/google/t5-xxl-ssm-nqo|35.2|
|T5-3b|https://huggingface.co/google/t5-3b-ssm-nqo|31.7|
|T5-11b|https://huggingface.co/google/t5-11b-ssm-nqo|34.8|


## Usage


The model can be used as follows for **closed book question answering**:


```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-large-ssm-nqo")
t5_tok = AutoTokenizer.from_pretrained("google/t5-large-ssm-nqo")

input_ids = t5_tok("When was Franklin D. Roosevelt born?", return_tensors="pt").input_ids
gen_output = t5_qa_model.generate(input_ids)[0]

print(t5_tok.decode(gen_output, skip_special_tokens=True))
```


## Abstract


It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge. We show that this approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions. To facilitate reproducibility and future work, we release our code and trained models at https://goo.gle/t5-cbqa.


![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/how_much_know_ledge_image.png)


model name: t5-large-ssm-nqo


model author: google


task: text2text-generation


tags: pytorch, tf, jax, t5, text2text-generation, en, dataset:c4, dataset:wikipedia, dataset:natural_questions, arxiv:2002.08909, arxiv:1910.10683, transformers, license:apache-2.0, autonlp_compatible


language: en
tags:


- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_0k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 0k


MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.


We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).


The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).


This is model #3, captured at step 0k (max: 2000k, i.e., 2M steps).


## Model Description


This model was captured during a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.


The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:


* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).


This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.


### How to use


Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_0k')
model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_0k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_0k')
model = BertModel.from_pretrained("google/multiberts-seed_3-step_0k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


```bibtex
@article{sellam2021multiberts,
  title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
  author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
  journal={arXiv preprint arXiv:2106.16163},
  year={2021}
}
```


model name: multiberts-seed_3-step_0k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_3, multiberts-seed_3-step_0k, license:apache-2.0, infinity_compatible


language: en
tags:


- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_800k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 800k


This is model #3, captured at step 800k (max: 2000k, i.e., 2M steps).


## Model Description


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_800k')
model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_800k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_800k')
model = BertModel.from_pretrained("google/multiberts-seed_3-step_800k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


model name: multiberts-seed_3-step_800k


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_3, multiberts-seed_3-step_800k, license:apache-2.0, infinity_compatible


language: en
datasets:


- c4
- wikipedia
- web_questions


## license: apache-2.0


[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.


The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.pdf)'s salient span masking objective on [Wikipedia](https://huggingface.co/datasets/wikipedia), and finally fine-tuned on [Web Questions (WQ)](https://huggingface.co/datasets/web_questions).


**Note**: The model was fine-tuned on 90% of the train splits of [Web Questions (WQ)](https://huggingface.co/datasets/web_questions) for 20k steps and validated on the held-out 10% of the train split.


Other community Checkpoints: [here](https://huggingface.co/models?search=ssm)


Paper: [How Much Knowledge Can You Pack
Into the Parameters of a Language Model?](https://arxiv.org/abs/1910.10683.pdf)


Authors: *Adam Roberts, Colin Raffel, Noam Shazeer*


## Results on Web Questions - Test Set


|Id | link | Exact Match  |
|---|---|---|
|**T5-11b**|**https://huggingface.co/google/t5-11b-ssm-wqo**|**40.8**|
|T5-xxl|https://huggingface.co/google/t5-xxl-ssm-wqo|42.8|


## Usage


The model can be used as follows for **closed book question answering**:


```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-11b-ssm-wqo")
t5_tok = AutoTokenizer.from_pretrained("google/t5-11b-ssm-wqo")

input_ids = t5_tok("When was Franklin D. Roosevelt born?", return_tensors="pt").input_ids
gen_output = t5_qa_model.generate(input_ids)[0]

print(t5_tok.decode(gen_output, skip_special_tokens=True))
```


## Abstract


It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge. We show that this approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions. To facilitate reproducibility and future work, we release our code and trained models at https://goo.gle/t5-cbqa.


![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/how_much_know_ledge_image.png)


model name: t5-11b-ssm-wqo


model author: google


tags: en, dataset:c4, dataset:wikipedia, dataset:web_questions, arxiv:2002.08909, arxiv:1910.10683, license:apache-2.0


language: en
tags:


- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_1900k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 1900k


MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.


We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).


The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).


This is model #3, captured at step 1900k (max: 2000k, i.e., 2M steps).


## Model Description


This model was captured during a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.


The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:


* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).


This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.


### How to use


Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_1900k')
model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_1900k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_1900k')
model = BertModel.from_pretrained("google/multiberts-seed_3-step_1900k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


```bibtex
@article{sellam2021multiberts,
  title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
  author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
  journal={arXiv preprint arXiv:2106.16163},
  year={2021}
}
```


model name: multiberts-seed_3-step_1900k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_3, multiberts-seed_3-step_1900k, license:apache-2.0, infinity_compatible


language: en
datasets:


- c4
tags:
- t5-lm-adapt


## license: apache-2.0


[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 - LM-Adapted


## Version 1.1 - LM-Adapted


[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) includes the following improvements compared to the original [T5 model](https://huggingface.co/t5-small):


- GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202).
- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.
- Pre-trained on C4 only without mixing in the downstream tasks.
- no parameter sharing between embedding and classifier layer
- "xl" and "xxl" replace "3B" and "11B". The model shapes are a bit different - larger `d_model` and smaller `num_heads` and `d_ff`.


and is pretrained on both the denoising and language modeling objective.


More specifically, this checkpoint is initialized from [T5 Version 1.1 - Small](https://huggingface.co/google/https://huggingface.co/google/t5-v1_1-small)
and then trained for an additional 100K steps on the LM objective discussed in the [T5 paper](https://arxiv.org/pdf/1910.10683.pdf).
This adaptation improves the ability of the model to be used for prompt tuning.


**Note**: A popular fine-tuned version of the *T5 Version 1.1 - LM Adapted* model is [BigScience's T0pp](https://huggingface.co/bigscience/T0pp).


Pretraining Dataset: [C4](https://huggingface.co/datasets/c4)


Other Community Checkpoints: [here](https://huggingface.co/models?other=t5-lm-adapt)


Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)


Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*


## Abstract


Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.


model name: t5-small-lm-adapt


model author: google


task: text2text-generation


tags: pytorch, tf, t5, text2text-generation, en, dataset:c4, arxiv:2002.05202, arxiv:1910.10683, transformers, t5-lm-adapt, license:apache-2.0, autonlp_compatible


language: en
tags:


- multiberts
- multiberts-seed_4
- multiberts-seed_4-step_1700k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 1700k


MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.


We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).


The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).


This is model #4, captured at step 1700k (max: 2000k, i.e., 2M steps).


## Model Description


This model was captured during a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.


The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:


* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).


This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.


### How to use


Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_1700k')
model = TFBertModel.from_pretrained("google/multiberts-seed_4-step_1700k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_1700k')
model = BertModel.from_pretrained("google/multiberts-seed_4-step_1700k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


```bibtex
@article{sellam2021multiberts,
  title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
  author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
  journal={arXiv preprint arXiv:2106.16163},
  year={2021}
}
```


model name: multiberts-seed_4-step_1700k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_4, multiberts-seed_4-step_1700k, license:apache-2.0, infinity_compatible


language: en
license: apache-2.0
datasets:


- bookcorpus
- wikipedia
- cc_news


# BigBird large model


BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.


It is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this [paper](https://arxiv.org/abs/2007.14062) and first released in this [repository](https://github.com/google-research/bigbird).


Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team.


## Model description


BigBird relies on **block sparse attention** instead of normal attention (i.e. BERT's attention) and can handle sequences up to a length of 4096 at a much lower compute cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question-answering with long contexts.


## How to use


Here is how to use this model to get the features of a given text in PyTorch:


```python
from transformers import BigBirdModel

# by default its in `block_sparse` mode with num_random_blocks=3, block_size=64
model = BigBirdModel.from_pretrained("google/bigbird-roberta-large")

# you can change `attention_type` to full attention like this:
model = BigBirdModel.from_pretrained("google/bigbird-roberta-large", attention_type="original_full")

# you can change `block_size` & `num_random_blocks` like this:
model = BigBirdModel.from_pretrained("google/bigbird-roberta-large", block_size=16, num_random_blocks=2)

text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Training Data


This model is pre-trained on four publicly available datasets: **Books**, **CC-News**, **Stories** and **Wikipedia**. It used same sentencepiece vocabulary as RoBERTa (which is in turn borrowed from GPT2).


## Training Procedure


Document longer than 4096 were split into multiple documents and documents that were much smaller than 4096 were joined. Following the original BERT training, 15% of tokens were masked and model is trained to predict the mask.


Model is warm started from RoBERTa’s checkpoint.


## BibTeX entry and citation info


```tex
@misc{zaheer2021big,
      title={Big Bird: Transformers for Longer Sequences},
      author={Manzil Zaheer and Guru Guruganesh and Avinava Dubey and Joshua Ainslie and Chris Alberti and Santiago Ontanon and Philip Pham and Anirudh Ravula and Qifan Wang and Li Yang and Amr Ahmed},
      year={2021},
      eprint={2007.14062},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```


model name: bigbird-roberta-large


model author: google


task: fill-mask


tags: pytorch, jax, big_bird, fill-mask, en, dataset:bookcorpus, dataset:wikipedia, dataset:cc_news, arxiv:2007.14062, transformers, license:apache-2.0, autonlp_compatible


language: en
tags:


- multiberts
- multiberts-seed_13
license: apache-2.0


# MultiBERTs - Seed 13


MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.


We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).


The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).


This is model #13.


## Model Description


This model is a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.


The intended uses, limitations, training data and training procedure are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:


* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).


This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.


### How to use


Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_13')
model = TFBertModel.from_pretrained("google/multiberts-seed_13")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_13')
model = BertModel.from_pretrained("google/multiberts-seed_13")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


```bibtex
@article{sellam2021multiberts,
  title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
  author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
  journal={arXiv preprint arXiv:2106.16163},
  year={2021}
}
```


model name: multiberts-seed_13


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_13, license:apache-2.0, infinity_compatible


language: en
tags:


- multiberts
- multiberts-seed_1
- multiberts-seed_1-step_1600k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1600k


This is model #1, captured at step 1600k (max: 2000k, i.e., 2M steps).


## Model Description


This model was captured during a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.


The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:


This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.


### How to use


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_1600k')
model = TFBertModel.from_pretrained("google/multiberts-seed_1-step_1600k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_1600k')
model = BertModel.from_pretrained("google/multiberts-seed_1-step_1600k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


model name: multiberts-seed_1-step_1600k


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_1, multiberts-seed_1-step_1600k, license:apache-2.0, infinity_compatible


- multiberts
- multiberts-seed_1
- multiberts-seed_1-step_2000k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 2000k


MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.


We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).


The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).


This is model #1, captured at step 2000k (max: 2000k, i.e., 2M steps).


## Model Description


* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).


Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_2000k')
model = TFBertModel.from_pretrained("google/multiberts-seed_1-step_2000k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_2000k')
model = BertModel.from_pretrained("google/multiberts-seed_1-step_2000k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


```bibtex
@article{sellam2021multiberts,
  title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
  author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
  journal={arXiv preprint arXiv:2106.16163},
  year={2021}
}
```


model name: multiberts-seed_1-step_2000k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_1, multiberts-seed_1-step_2000k, license:apache-2.0, infinity_compatible


language: en
datasets:


- c4
- wikipedia
- natural_questions


pipeline_tag: text2text-generation


## license: apache-2.0


[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.


The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.pdf)'s salient span masking objective on [Wikipedia](https://huggingface.co/datasets/wikipedia), and finally fine-tuned on [Natural Questions (NQ)](https://huggingface.co/datasets/natural_questions).


**Note**: The model was fine-tuned on 100% of the train splits of [Natural Questions (NQ)](https://huggingface.co/datasets/natural_questions) for 10k steps.


Other community Checkpoints: [here](https://huggingface.co/models?search=ssm)


Paper: [How Much Knowledge Can You Pack
Into the Parameters of a Language Model?](https://arxiv.org/abs/1910.10683.pdf)


Authors: *Adam Roberts, Colin Raffel, Noam Shazeer*


## Results on Natural Questions - Test Set


|Id | link | Exact Match  |
|---|---|---|
|T5-small|https://huggingface.co/google/t5-small-ssm-nq|25.5|
|T5-large|https://huggingface.co/google/t5-large-ssm-nq|30.4|
|**T5-xl**|**https://huggingface.co/google/t5-xl-ssm-nq**|**35.6**|
|T5-xxl|https://huggingface.co/google/t5-xxl-ssm-nq|37.9|
|T5-3b|https://huggingface.co/google/t5-3b-ssm-nq|33.2|
|T5-11b|https://huggingface.co/google/t5-11b-ssm-nq|36.6|


## Usage


The model can be used as follows for **closed book question answering**:


```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-xl-ssm-nq")
t5_tok = AutoTokenizer.from_pretrained("google/t5-xl-ssm-nq")

input_ids = t5_tok("When was Franklin D. Roosevelt born?", return_tensors="pt").input_ids
gen_output = t5_qa_model.generate(input_ids)[0]

print(t5_tok.decode(gen_output, skip_special_tokens=True))
```


## Abstract


It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge. We show that this approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions. To facilitate reproducibility and future work, we release our code and trained models at https://goo.gle/t5-cbqa.


![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/how_much_know_ledge_image.png)


model name: t5-xl-ssm-nq


model author: google


task: text2text-generation


tags: pytorch, tf, t5, text2text-generation, en, dataset:c4, dataset:wikipedia, dataset:natural_questions, arxiv:2002.08909, arxiv:1910.10683, transformers, license:apache-2.0, autonlp_compatible


language: en
tags:


- multiberts
- multiberts-seed_4
- multiberts-seed_4-step_2000k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 2000k


MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.


We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).


The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).


This is model #4, captured at step 2000k (max: 2000k, i.e., 2M steps).


## Model Description


This model was captured during a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.


The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:


* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).


This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.


### How to use


Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_2000k')
model = TFBertModel.from_pretrained("google/multiberts-seed_4-step_2000k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_2000k')
model = BertModel.from_pretrained("google/multiberts-seed_4-step_2000k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


```bibtex
@article{sellam2021multiberts,
  title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
  author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
  journal={arXiv preprint arXiv:2106.16163},
  year={2021}
}
```


model name: multiberts-seed_4-step_2000k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_4, multiberts-seed_4-step_2000k, license:apache-2.0, infinity_compatible


language: en
tags:


- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_140k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 140k


This is model #2, captured at step 140k (max: 2000k, i.e., 2M steps).


## Model Description


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_140k')
model = TFBertModel.from_pretrained("google/multiberts-seed_2-step_140k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_140k')
model = BertModel.from_pretrained("google/multiberts-seed_2-step_140k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


model name: multiberts-seed_2-step_140k


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_2, multiberts-seed_2-step_140k, license:apache-2.0, infinity_compatible


language: multilingual
datasets:


- mc4


## license: apache-2.0


[Google's mT5](https://github.com/google-research/multilingual-t5)


mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages:


Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu.


**Note**: mT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task.


Pretraining Dataset: [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual)


Other Community Checkpoints: [here](https://huggingface.co/models?search=mt5)


Paper: [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934)


Authors: *Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel*


## Abstract


The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We describe the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. All of the code and model checkpoints used in this work are publicly available.


model name: mt5-base


model author: google


task: text2text-generation


tags: pytorch, tf, jax, mt5, text2text-generation, multilingual, dataset:mc4, arxiv:2010.11934, transformers, license:apache-2.0, autonlp_compatible


language: en
tags:


- multiberts
- multiberts-seed_0
- multiberts-seed_0-step_60k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 60k


MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.


We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).


The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).


This is model #0, captured at step 60k (max: 2000k, i.e., 2M steps).


## Model Description


This model was captured during a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.


The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:


* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).


This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.


### How to use


Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_60k')
model = TFBertModel.from_pretrained("google/multiberts-seed_0-step_60k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_60k')
model = BertModel.from_pretrained("google/multiberts-seed_0-step_60k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


```bibtex
@article{sellam2021multiberts,
  title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
  author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
  journal={arXiv preprint arXiv:2106.16163},
  year={2021}
}
```


model name: multiberts-seed_0-step_60k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_0, multiberts-seed_0-step_60k, license:apache-2.0, infinity_compatible


language: en
tags:


- summarization


### Pegasus Models


See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)


Original TF 1 code [here](https://github.com/google-research/pegasus)


Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019


Maintained by: [@sshleifer](https://twitter.com/sam_shleifer)


Task: Summarization


The following is copied from the authors' README.


# Mixed & Stochastic Checkpoints


We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table.


| dataset | C4 | HugeNews | Mixed & Stochastic|
| ---- | ---- | ---- | ----|
| xsum | 45.20/22.06/36.99 | 47.21/24.56/39.25 | 47.60/24.83/39.64|
| cnn_dailymail | 43.90/21.20/40.76 | 44.17/21.47/41.11 | 44.16/21.56/41.30|
| newsroom | 45.07/33.39/41.28 | 45.15/33.51/41.33 | 45.98/34.20/42.18|
| multi_news | 46.74/17.95/24.26 | 47.52/18.72/24.91 | 47.65/18.75/24.95|
| gigaword | 38.75/19.96/36.14 | 39.12/19.86/36.24 | 39.65/20.47/36.76|
| wikihow | 43.07/19.70/34.79 | 41.35/18.51/33.42 | 46.39/22.12/38.41 *|
| reddit_tifu | 26.54/8.94/21.64 | 26.63/9.01/21.60 | 27.99/9.81/22.94|
| big_patent | 53.63/33.16/42.25 | 53.41/32.89/42.07 | 52.29/33.08/41.66 *|
| arxiv | 44.70/17.27/25.80 | 44.67/17.18/25.73 | 44.21/16.95/25.67|
| pubmed | 45.49/19.90/27.69 | 45.09/19.56/27.42 | 45.97/20.15/28.25|
| aeslc | 37.69/21.85/36.84 | 37.40/21.22/36.45 | 37.68/21.25/36.51|
| billsum | 57.20/39.56/45.80 | 57.31/40.19/45.82 | 59.67/41.58/47.59|


The "Mixed & Stochastic" model has the following changes:


- trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples).
- trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity).
- the model uniformly sample a gap sentence ratio between 15% and 45%.
- importance sentences are sampled using a 20% uniform noise to importance scores.
- the sentencepiece tokenizer is updated to be able to encode newline character.


(*) the numbers of wikihow and big_patent datasets are not comparable because of change in tokenization and data:


- wikihow dataset contains newline characters which is useful for paragraph segmentation, the C4 and HugeNews model's sentencepiece tokenizer doesn't encode newline and loose this information.
- we update the BigPatent dataset to preserve casing, some format cleanings are also changed, please refer to change in TFDS.


The "Mixed & Stochastic" model has the following changes (from pegasus-large in the paper):


trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples).
trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity).
the model uniformly sample a gap sentence ratio between 15% and 45%.
importance sentences are sampled using a 20% uniform noise to importance scores.
the sentencepiece tokenizer is updated to be able to encode newline character.


Citation


```

@misc{zhang2019pegasus,
    title={PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization},
    author={Jingqing Zhang and Yao Zhao and Mohammad Saleh and Peter J. Liu},
    year={2019},
    eprint={1912.08777},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```


model name: pegasus-newsroom


model author: google


task: summarization


tags: pytorch, pegasus, text2text-generation, en, arxiv:1912.08777, transformers, summarization, autonlp_compatible


language: en
tags:


- multiberts
- multiberts-seed_9
license: apache-2.0


# MultiBERTs - Seed 9


MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.


We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).


The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).


This is model #9.


## Model Description


This model is a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.


The intended uses, limitations, training data and training procedure are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:


* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).


This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.


### How to use


Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_9')
model = TFBertModel.from_pretrained("google/multiberts-seed_9")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_9')
model = BertModel.from_pretrained("google/multiberts-seed_9")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


```bibtex
@article{sellam2021multiberts,
  title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
  author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
  journal={arXiv preprint arXiv:2106.16163},
  year={2021}
}
```


model name: multiberts-seed_9


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_9, license:apache-2.0, infinity_compatible


language: en
tags:


- multiberts
- multiberts-seed_0
- multiberts-seed_0-step_100k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 100k


This is model #0, captured at step 100k (max: 2000k, i.e., 2M steps).


## Model Description


This model was captured during a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.


The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:


This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.


### How to use


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_100k')
model = TFBertModel.from_pretrained("google/multiberts-seed_0-step_100k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_100k')
model = BertModel.from_pretrained("google/multiberts-seed_0-step_100k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


model name: multiberts-seed_0-step_100k


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_0, multiberts-seed_0-step_100k, license:apache-2.0, infinity_compatible


- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_1300k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 1300k


MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.


We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).


The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).


This is model #2, captured at step 1300k (max: 2000k, i.e., 2M steps).


## Model Description


* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).


Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_1300k')
model = TFBertModel.from_pretrained("google/multiberts-seed_2-step_1300k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_1300k')
model = BertModel.from_pretrained("google/multiberts-seed_2-step_1300k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


```bibtex
@article{sellam2021multiberts,
  title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
  author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
  journal={arXiv preprint arXiv:2106.16163},
  year={2021}
}
```


model name: multiberts-seed_2-step_1300k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_2, multiberts-seed_2-step_1300k, license:apache-2.0, infinity_compatible


language: multilingual
license: apache-2.0
datasets:


- wikipedia


# RemBERT (for classification)


Pretrained RemBERT model on 110 languages using a masked language modeling (MLM) objective. It was introduced in the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/abs/2010.12821). A direct export of the model checkpoint was first made available in [this repository](https://github.com/google-research/google-research/tree/master/rembert). This version of the checkpoint is lightweight since it is meant to be finetuned for classification and excludes the output embedding weights.


## Model description


RemBERT's main difference with mBERT is that the input and output embeddings are not tied. Instead, RemBERT uses small input embeddings and larger output embeddings. This makes the model more efficient since the output embeddings are discarded during fine-tuning. It is also more accurate, especially when reinvesting the input embeddings' parameters into the core model, as is done on RemBERT.


## Intended uses & limitations


You should fine-tune this model for your downstream task. It is meant to be a general-purpose model, similar to mBERT. In our [paper](https://arxiv.org/abs/2010.12821), we have successfully applied this model to tasks such as classification, question answering, NER, POS-tagging. For tasks such as text generation you should look at models like GPT2.


## Training data


The RemBERT model was pretrained on multilingual Wikipedia data over 110 languages. The full language list is on [this repository](https://github.com/google-research/google-research/tree/master/rembert)


### BibTeX entry and citation info


```bibtex
@inproceedings{DBLP:conf/iclr/ChungFTJR21,
  author    = {Hyung Won Chung and
               Thibault F{\'{e}}vry and
               Henry Tsai and
               Melvin Johnson and
               Sebastian Ruder},
  title     = {Rethinking Embedding Coupling in Pre-trained Language Models},
  booktitle = {9th International Conference on Learning Representations, {ICLR} 2021,
               Virtual Event, Austria, May 3-7, 2021},
  publisher = {OpenReview.net},
  year      = {2021},
  url       = {https://openreview.net/forum?id=xpFFI\_NtgpW},
  timestamp = {Wed, 23 Jun 2021 17:36:39 +0200},
  biburl    = {https://dblp.org/rec/conf/iclr/ChungFTJR21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```


model name: rembert


tags: pytorch, tf, rembert, multilingual, dataset:wikipedia, arxiv:2010.12821, transformers, license:apache-2.0


language: en
tags:


- multiberts
- multiberts-seed_1
- multiberts-seed_1-step_1100k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1100k


MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.


We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).


The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).


This is model #1, captured at step 1100k (max: 2000k, i.e., 2M steps).


## Model Description


This model was captured during a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.


The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:


* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).


This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.


### How to use


Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_1100k')
model = TFBertModel.from_pretrained("google/multiberts-seed_1-step_1100k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_1100k')
model = BertModel.from_pretrained("google/multiberts-seed_1-step_1100k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


```bibtex
@article{sellam2021multiberts,
  title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
  author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
  journal={arXiv preprint arXiv:2106.16163},
  year={2021}
}
```


model name: multiberts-seed_1-step_1100k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_1, multiberts-seed_1-step_1100k, license:apache-2.0, infinity_compatible


language: en
tags:


- multiberts
- multiberts-seed_4
- multiberts-seed_4-step_1800k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 1800k


This is model #4, captured at step 1800k (max: 2000k, i.e., 2M steps).


## Model Description


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_1800k')
model = TFBertModel.from_pretrained("google/multiberts-seed_4-step_1800k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_1800k')
model = BertModel.from_pretrained("google/multiberts-seed_4-step_1800k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


model name: multiberts-seed_4-step_1800k


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_4, multiberts-seed_4-step_1800k, license:apache-2.0, infinity_compatible


- tapas
license: apache-2.0
datasets:
- msr_sqa


# TAPAS medium model fine-tuned on Sequential Question Answering (SQA)


This model has 2 versions which can be used. The default version corresponds to the `tapas_sqa_inter_masklm_medium_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned on [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253). It uses relative position embeddings (i.e. resetting the position index at every cell of the table).


The other (non-default) version which can be used is:


- `no_reset`, which corresponds to `tapas_sqa_inter_masklm_medium` (intermediate pre-training, absolute position embeddings).


Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
the Hugging Face team and contributors.


## Results on SQA - Dev Accuracy


Size     |  Reset  | Dev Accuracy | Link
-------- | --------| -------- | ----
LARGE | noreset | 0.7223 | [tapas-large-finetuned-sqa (absolute pos embeddings)](https://huggingface.co/google/tapas-large-finetuned-sqa/tree/no_reset)
LARGE | reset | 0.7289 | [tapas-large-finetuned-sqa](https://huggingface.co/google/tapas-large-finetuned-sqa/tree/main)
BASE | noreset | 0.6737 | [tapas-base-finetuned-sqa (absolute pos embeddings)](https://huggingface.co/google/tapas-base-finetuned-sqa/tree/no_reset)
BASE | reset | 0.6874 | [tapas-base-finetuned-sqa](https://huggingface.co/google/tapas-base-finetuned-sqa/tree/main)
**MEDIUM** | **noreset** | **0.6464** | [tapas-medium-finetuned-sqa (absolute pos embeddings)](https://huggingface.co/google/tapas-medium-finetuned-sqa/tree/no_reset)
**MEDIUM** | **reset** | **0.6561** | [tapas-medium-finetuned-sqa](https://huggingface.co/google/tapas-medium-finetuned-sqa/tree/main)
SMALL | noreset | 0.5876 | [tapas-small-finetuned-sqa (absolute pos embeddings)](https://huggingface.co/google/tapas-small-finetuned-sqa/tree/no_reset)
SMALL | reset | 0.6155 | [tapas-small-finetuned-sqa](https://huggingface.co/google/tapas-small-finetuned-sqa/tree/main)
MINI | noreset | 0.4574 | [tapas-mini-finetuned-sqa (absolute pos embeddings)](https://huggingface.co/google/tapas-mini-finetuned-sqa/tree/no_reset)
MINI | reset | 0.5148 | [tapas-mini-finetuned-sqa](https://huggingface.co/google/tapas-mini-finetuned-sqa/tree/main))
TINY | noreset | 0.2004 | [tapas-tiny-finetuned-sqa (absolute pos embeddings)](https://huggingface.co/google/tapas-tiny-finetuned-sqa/tree/no_reset)
TINY | reset | 0.2375 | [tapas-tiny-finetuned-sqa](https://huggingface.co/google/tapas-tiny-finetuned-sqa/tree/main)


## Model description


TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion.
This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it
can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:


- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in
the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words.
This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other,
or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional
representation of a table and associated text.
- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating
a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence
is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements.


This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used
to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed
or refuted by the contents of a table. Fine-tuning is done by adding a cell selection head on top of the pre-trained model, and then jointly
train this randomly initialized classification head with the base model on SQA.


## Intended uses & limitations


You can use this model for answering questions related to a table in a conversational set-up.


For code examples, we refer to the documentation of TAPAS on the HuggingFace website.


## Training procedure


### Preprocessing


The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:


```
[CLS] Question [SEP] Flattened table [SEP]
```


### Fine-tuning


The model was fine-tuned on 32 Cloud TPU v3 cores for 200,000 steps with maximum sequence length 512 and batch size of 128.
In this setup, fine-tuning takes around 20 hours. The optimizer used is Adam with a learning rate of 1.25e-5, and a warmup ratio
of 0.2. An inductive bias is added such that the model only selects cells of the same column. This is reflected by the
`select_one_column` parameter of `TapasConfig`. See also table 12 of the [original paper](https://arxiv.org/abs/2004.02349).


### BibTeX entry and citation info


```bibtex
@misc{herzig2020tapas,
      title={TAPAS: Weakly Supervised Table Parsing via Pre-training},
      author={Jonathan Herzig and PaweÅ‚ Krzysztof Nowak and Thomas MÃ¼ller and Francesco Piccinno and Julian Martin Eisenschlos},
      year={2020},
      eprint={2004.02349},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}
```


```bibtex
@misc{eisenschlos2020understanding,
      title={Understanding tables with intermediate pre-training},
      author={Julian Martin Eisenschlos and Syrine Krichene and Thomas MÃ¼ller},
      year={2020},
      eprint={2010.00571},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


```bibtex
@InProceedings{iyyer2017search-based,
author = {Iyyer, Mohit and Yih, Scott Wen-tau and Chang, Ming-Wei},
title = {Search-based Neural Structured Learning for Sequential Question Answering},
booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics},
year = {2017},
month = {July},
abstract = {Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semi-structured tables from Wikipedia, with 17,553 question-answer pairs in total. To solve this sequential question answering task, we propose a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions.},
publisher = {Association for Computational Linguistics},
url = {https://www.microsoft.com/en-us/research/publication/search-based-neural-structured-learning-sequential-question-answering/},
}
```


model name: tapas-medium-finetuned-sqa


model author: google


task: table-question-answering


tags: pytorch, tf, tapas, table-question-answering, en, dataset:msr_sqa, arxiv:2004.02349, arxiv:2010.00571, transformers, license:apache-2.0


language: en
tags:


- multiberts
- multiberts-seed_1
- multiberts-seed_1-step_800k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 800k


MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.


We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).


The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).


This is model #1, captured at step 800k (max: 2000k, i.e., 2M steps).


## Model Description


This model was captured during a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.


The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:


* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).


This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.


### How to use


Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_800k')
model = TFBertModel.from_pretrained("google/multiberts-seed_1-step_800k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_800k')
model = BertModel.from_pretrained("google/multiberts-seed_1-step_800k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


```bibtex
@article{sellam2021multiberts,
  title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
  author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
  journal={arXiv preprint arXiv:2106.16163},
  year={2021}
}
```


model name: multiberts-seed_1-step_800k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_1, multiberts-seed_1-step_800k, license:apache-2.0, infinity_compatible


language: en
tags:


- multiberts
- multiberts-seed_0
- multiberts-seed_0-step_1600k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1600k


This is model #0, captured at step 1600k (max: 2000k, i.e., 2M steps).


## Model Description


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_1600k')
model = TFBertModel.from_pretrained("google/multiberts-seed_0-step_1600k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_1600k')
model = BertModel.from_pretrained("google/multiberts-seed_0-step_1600k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


model name: multiberts-seed_0-step_1600k


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_0, multiberts-seed_0-step_1600k, license:apache-2.0, infinity_compatible


- multiberts
- multiberts-seed_1
- multiberts-seed_1-step_1800k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1800k


MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.


We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).


The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).


This is model #1, captured at step 1800k (max: 2000k, i.e., 2M steps).


This model was captured during a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.


The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:


* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).


This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.


### How to use


Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_1800k')
model = TFBertModel.from_pretrained("google/multiberts-seed_1-step_1800k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_1800k')
model = BertModel.from_pretrained("google/multiberts-seed_1-step_1800k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


```bibtex
@article{sellam2021multiberts,
  title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
  author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
  journal={arXiv preprint arXiv:2106.16163},
  year={2021}
}
```


model name: multiberts-seed_1-step_1800k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_1, multiberts-seed_1-step_1800k, license:apache-2.0, infinity_compatible


language: en
tags:


- multiberts
- multiberts-seed_4
- multiberts-seed_4-step_800k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 800k


This is model #4, captured at step 800k (max: 2000k, i.e., 2M steps).


## Model Description


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_800k')
model = TFBertModel.from_pretrained("google/multiberts-seed_4-step_800k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_800k')
model = BertModel.from_pretrained("google/multiberts-seed_4-step_800k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


model name: multiberts-seed_4-step_800k


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_4, multiberts-seed_4-step_800k, license:apache-2.0, infinity_compatible


- multiberts
- multiberts-seed_4
- multiberts-seed_4-step_80k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 80k


MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.


We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).


The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).


This is model #4, captured at step 80k (max: 2000k, i.e., 2M steps).


This model was captured during a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.


The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:


* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).


This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.


### How to use


Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_80k')
model = TFBertModel.from_pretrained("google/multiberts-seed_4-step_80k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_80k')
model = BertModel.from_pretrained("google/multiberts-seed_4-step_80k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


```bibtex
@article{sellam2021multiberts,
  title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
  author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
  journal={arXiv preprint arXiv:2106.16163},
  year={2021}
}
```


model name: multiberts-seed_4-step_80k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_4, multiberts-seed_4-step_80k, license:apache-2.0, infinity_compatible


language: en
tags:


- multiberts
- multiberts-seed_7
license: apache-2.0


# MultiBERTs - Seed 7


This is model #7.


## Model Description


This model is a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.


The intended uses, limitations, training data and training procedure are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:


This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.


### How to use


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_7')
model = TFBertModel.from_pretrained("google/multiberts-seed_7")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_7')
model = BertModel.from_pretrained("google/multiberts-seed_7")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


model name: multiberts-seed_7


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_7, license:apache-2.0, infinity_compatible


thumbnail: https://huggingface.co/front/thumbnails/google.png


## license: apache-2.0


# BERT Miniatures


This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).


We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher.


Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.


You can download the 24 BERT miniatures either from the [official BERT Github page](https://github.com/google-research/bert/), or via HuggingFace from the links below:


|   |H=128|H=256|H=512|H=768|
|---|:---:|:---:|:---:|:---:|
| **L=2**  |[**2/128 (BERT-Tiny)**](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2)|[2/256](https://huggingface.co/google/bert_uncased_L-2_H-256_A-4)|[2/512](https://huggingface.co/google/bert_uncased_L-2_H-512_A-8)|[2/768](https://huggingface.co/google/bert_uncased_L-2_H-768_A-12)|
| **L=4**  |[4/128](https://huggingface.co/google/bert_uncased_L-4_H-128_A-2)|[**4/256 (BERT-Mini)**](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4)|[**4/512 (BERT-Small)**](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8)|[4/768](https://huggingface.co/google/bert_uncased_L-4_H-768_A-12)|
| **L=6**  |[6/128](https://huggingface.co/google/bert_uncased_L-6_H-128_A-2)|[6/256](https://huggingface.co/google/bert_uncased_L-6_H-256_A-4)|[6/512](https://huggingface.co/google/bert_uncased_L-6_H-512_A-8)|[6/768](https://huggingface.co/google/bert_uncased_L-6_H-768_A-12)|
| **L=8**  |[8/128](https://huggingface.co/google/bert_uncased_L-8_H-128_A-2)|[8/256](https://huggingface.co/google/bert_uncased_L-8_H-256_A-4)|[**8/512 (BERT-Medium)**](https://huggingface.co/google/bert_uncased_L-8_H-512_A-8)|[8/768](https://huggingface.co/google/bert_uncased_L-8_H-768_A-12)|
| **L=10** |[10/128](https://huggingface.co/google/bert_uncased_L-10_H-128_A-2)|[10/256](https://huggingface.co/google/bert_uncased_L-10_H-256_A-4)|[10/512](https://huggingface.co/google/bert_uncased_L-10_H-512_A-8)|[10/768](https://huggingface.co/google/bert_uncased_L-10_H-768_A-12)|
| **L=12** |[12/128](https://huggingface.co/google/bert_uncased_L-12_H-128_A-2)|[12/256](https://huggingface.co/google/bert_uncased_L-12_H-256_A-4)|[12/512](https://huggingface.co/google/bert_uncased_L-12_H-512_A-8)|[**12/768 (BERT-Base)**](https://huggingface.co/google/bert_uncased_L-12_H-768_A-12)|


Note that the BERT-Base model in this release is included for completeness only; it was re-trained under the same regime as the original model.


Here are the corresponding GLUE scores on the test set:


For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained for 4 epochs:


- batch sizes: 8, 16, 32, 64, 128
- learning rates: 3e-4, 1e-4, 5e-5, 3e-5


If you use these models, please cite the following paper:


```
@article{turc2019,
  title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models},
  author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  journal={arXiv preprint arXiv:1908.08962v2 },
  year={2019}
}
```


model name: bert_uncased_L-8_H-512_A-8


model author: google


tags: pytorch, jax, bert, arxiv:1908.08962, transformers, license:apache-2.0, infinity_compatible


language: en
tags:


- tapas
license: apache-2.0
datasets:
- wtq


# TAPAS base model fine-tuned on WikiTable Questions (WTQ)


This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned in a chain on [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253), [WikiSQL](https://github.com/salesforce/WikiSQL) and finally [WTQ](https://github.com/ppasupat/WikiTableQuestions). It uses relative position embeddings (i.e. resetting the position index at every cell of the table).


The other (non-default) version which can be used is:


- `no_reset`, which corresponds to `tapas_wtq_wikisql_sqa_inter_masklm_base` (intermediate pre-training, absolute position embeddings).


Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
the Hugging Face team and contributors.


## Results


Size     |  Reset  | Dev Accuracy | Link
-------- | --------| -------- | ----
LARGE | noreset | 0.5062 | [tapas-large-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-large-finetuned-wtq/tree/no_reset)
LARGE | reset | 0.5097 | [tapas-large-finetuned-wtq](https://huggingface.co/google/tapas-large-finetuned-wtq/tree/main)
**BASE** | **noreset** | **0.4525** | [tapas-base-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-base-finetuned-wtq/tree/no_reset)
**BASE** | **reset** | **0.4638** | [tapas-base-finetuned-wtq](https://huggingface.co/google/tapas-base-finetuned-wtq/tree/main)
MEDIUM | noreset | 0.4324 | [tapas-medium-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-medium-finetuned-wtq/tree/no_reset)
MEDIUM | reset | 0.4324 | [tapas-medium-finetuned-wtq](https://huggingface.co/google/tapas-medium-finetuned-wtq/tree/main)
SMALL | noreset | 0.3681 | [tapas-small-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-small-finetuned-wtq/tree/no_reset)
SMALL | reset | 0.3762 | [tapas-small-finetuned-wtq](https://huggingface.co/google/tapas-small-finetuned-wtq/tree/main)
MINI | noreset | 0.2783 | [tapas-mini-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-mini-finetuned-wtq/tree/no_reset)
MINI | reset | 0.2854 | [tapas-mini-finetuned-wtq](https://huggingface.co/google/tapas-mini-finetuned-wtq/tree/main)
TINY | noreset | 0.0823 | [tapas-tiny-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-tiny-finetuned-wtq/tree/no_reset)
TINY | reset | 0.1039 | [tapas-tiny-finetuned-wtq](https://huggingface.co/google/tapas-tiny-finetuned-wtq/tree/main)


## Model description


TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion.
This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it
can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives: