---
language: en
tags:
- tapas
- sequence-classification
license: apache-2.0
datasets:
- tab_fact

---

# TAPAS small model fine-tuned on Tabular Fact Checking (TabFact)

This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_small_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 [TabFact](https://github.com/wenhuchen/Table-Fact-Checking). It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table).

The other (non-default) version which can be used is the one with absolute position embeddings:
- `no_reset`, which corresponds to `tapas_tabfact_inter_masklm_small`

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.

## 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 classification head on top of the pre-trained model, and then
jointly train this randomly initialized classification head with the base model on TabFact.

## Intended uses & limitations

You can use this model for classifying whether a sentence is supported or refuted by the contents of a table.

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] Sentence [SEP] Flattened table [SEP]
```

### Fine-tuning

The model was fine-tuned on 32 Cloud TPU v3 cores for 80,000 steps with maximum sequence length 512 and batch size of 512.
In this setup, fine-tuning takes around 14 hours. The optimizer used is Adam with a learning rate of 2e-5, and a warmup
ratio of 0.05. See the [paper](https://arxiv.org/abs/2010.00571) for more details (appendix A2).

### BibTeX entry and citation info

```bibtex
@misc{herzig2020tapas,
      title={TAPAS: Weakly Supervised Table Parsing via Pre-training},
      author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller 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 Müller},
      year={2020},
      eprint={2010.00571},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

```bibtex
@inproceedings{2019TabFactA,
  title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
  author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
  booktitle = {International Conference on Learning Representations (ICLR)},
  address = {Addis Ababa, Ethiopia},
  month = {April},
  year = {2020}
}
```


---
language: en
thumbnail: https://huggingface.co/front/thumbnails/google.png

license: apache-2.0
---

## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.

For a detailed description and experimental results, please refer to our paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).

This repository contains code to pre-train ELECTRA, including small ELECTRA models on a single GPU. It also supports fine-tuning ELECTRA on downstream tasks including classification tasks (e.g,. [GLUE](https://gluebenchmark.com/)), QA tasks (e.g., [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)), and sequence tagging tasks (e.g., [text chunking](https://www.clips.uantwerpen.be/conll2000/chunking/)).

## How to use the discriminator in `transformers`

```python
from transformers import ElectraForPreTraining, ElectraTokenizerFast
import torch

discriminator = ElectraForPreTraining.from_pretrained("google/electra-large-discriminator")
tokenizer = ElectraTokenizerFast.from_pretrained("google/electra-large-discriminator")

sentence = "The quick brown fox jumps over the lazy dog"
fake_sentence = "The quick brown fox fake over the lazy dog"

fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)

[print("%7s" % token, end="") for token in fake_tokens]

[print("%7s" % int(prediction), end="") for prediction in predictions.tolist()]
```


---
language: en
tags:
-   multiberts
-   multiberts-seed_3
-   multiberts-seed_3-step_80k
license: apache-2.0
---

# MultiBERTs, Intermediate Checkpoint - Seed 3, 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 #3, captured at step 80k (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_80k')
model = TFBertModel.from_pretrained("google/multiberts-seed_3-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_3-step_80k')
model = BertModel.from_pretrained("google/multiberts-seed_3-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}
}
```


---
language: en
tags:
-   multiberts
-   multiberts-seed_12
license: apache-2.0
---

# MultiBERTs - Seed 12

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 #12.

## 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_12')
model = TFBertModel.from_pretrained("google/multiberts-seed_12")
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_12')
model = BertModel.from_pretrained("google/multiberts-seed_12")
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}
}
```


---
language: en
datasets:
- c4
- wikipedia
- trivia_qa

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 [Trivia QA (TQA)](https://huggingface.co/datasets/trivia_qa).

**Note**: The model was fine-tuned on 100% of the train splits of [Trivia QA (TQA)](https://huggingface.co/datasets/trivia_qa) for 10 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 Trivia QA - Test Set

|Id | link | Exact Match  |
|---|---|---|
|T5-11b|https://huggingface.co/google/t5-large-ssm-tqa|60.5|
|**T5-xxl**|**https://huggingface.co/google/t5-xxl-ssm-tqa**|**61.6**|

## 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-xxl-ssm-tqa")
t5_tok = AutoTokenizer.from_pretrained("google/t5-xxl-ssm-tqa")

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)


---
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)**][2_128]|[2/256][2_256]|[2/512][2_512]|[2/768][2_768]|
| **L=4**  |[4/128][4_128]|[**4/256 (BERT-Mini)**][4_256]|[**4/512 (BERT-Small)**][4_512]|[4/768][4_768]|
| **L=6**  |[6/128][6_128]|[6/256][6_256]|[6/512][6_512]|[6/768][6_768]|
| **L=8**  |[8/128][8_128]|[8/256][8_256]|[**8/512 (BERT-Medium)**][8_512]|[8/768][8_768]|
| **L=10** |[10/128][10_128]|[10/256][10_256]|[10/512][10_512]|[10/768][10_768]|
| **L=12** |[12/128][12_128]|[12/256][12_256]|[12/512][12_512]|[**12/768 (BERT-Base)**][12_768]|

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:

|Model|Score|CoLA|SST-2|MRPC|STS-B|QQP|MNLI-m|MNLI-mm|QNLI(v2)|RTE|WNLI|AX|
|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|BERT-Tiny|64.2|0.0|83.2|81.1/71.1|74.3/73.6|62.2/83.4|70.2|70.3|81.5|57.2|62.3|21.0|
|BERT-Mini|65.8|0.0|85.9|81.1/71.8|75.4/73.3|66.4/86.2|74.8|74.3|84.1|57.9|62.3|26.1|
|BERT-Small|71.2|27.8|89.7|83.4/76.2|78.8/77.0|68.1/87.0|77.6|77.0|86.4|61.8|62.3|28.6|
|BERT-Medium|73.5|38.0|89.6|86.6/81.6|80.4/78.4|69.6/87.9|80.0|79.1|87.7|62.2|62.3|30.5|

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}
}
```

[2_128]: 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
[4_128]: https://huggingface.co/google/bert_uncased_L-4_H-128_A-2
[4_256]: https://huggingface.co/google/bert_uncased_L-4_H-256_A-4
[4_512]: 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
[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
[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]: 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
[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
[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]: https://huggingface.co/google/bert_uncased_L-12_H-768_A-12


---
language: en
tags:
-   multiberts
-   multiberts-seed_10
license: apache-2.0
---

# MultiBERTs - Seed 10

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 #10.

## 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_10')
model = TFBertModel.from_pretrained("google/multiberts-seed_10")
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_10')
model = BertModel.from_pretrained("google/multiberts-seed_10")
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}
}
```


---
language: en
tags:
-   multiberts
-   multiberts-seed_4
-   multiberts-seed_4-step_1600k
license: apache-2.0
---

# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 1600k

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 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:

*   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_1600k')
model = TFBertModel.from_pretrained("google/multiberts-seed_4-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_4-step_1600k')
model = BertModel.from_pretrained("google/multiberts-seed_4-step_1600k")
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}
}
```


---
language: en
tags:
-   multiberts
-   multiberts-seed_0
-   multiberts-seed_0-step_500k
license: apache-2.0
---

# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 500k

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 500k (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_500k')
model = TFBertModel.from_pretrained("google/multiberts-seed_0-step_500k")
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_500k')
model = BertModel.from_pretrained("google/multiberts-seed_0-step_500k")
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}
}
```


---
language: en
tags:
-   multiberts
-   multiberts-seed_3
-   multiberts-seed_3-step_1300k
license: apache-2.0
---

# MultiBERTs, Intermediate Checkpoint - Seed 3, 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 #3, captured at step 1300k (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_1300k')
model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_1300k")
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_1300k')
model = BertModel.from_pretrained("google/multiberts-seed_3-step_1300k")
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}
}
```


---
language: en
tags:
-   multiberts
-   multiberts-seed_2
-   multiberts-seed_2-step_400k
license: apache-2.0
---

# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 400k

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 400k (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_2-step_400k')
model = TFBertModel.from_pretrained("google/multiberts-seed_2-step_400k")
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_2-step_400k')
model = BertModel.from_pretrained("google/multiberts-seed_2-step_400k")
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}
}
```


---
language: en
tags:
- tapas
- TapasModel
license: apache-2.0
---

# TAPAS base model

This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_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. It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table).

The other (non-default) version which can be used is the one with absolute position embeddings:
- `revision="no_reset"`, which corresponds to `tapas_inter_masklm_base`

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.

## 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 one or more classification heads on top of the pre-trained model, and then
jointly train these randomly initialized classification heads with the base model on a downstream task.

## Intended uses & limitations

You can use the raw model for getting hidden representatons about table-question pairs, but it's mostly intended to be fine-tuned on a downstream task such as question answering or sequence classification. See the [model hub](https://huggingface.co/models?filter=tapas) to look for fine-tuned versions on a task that interests you.

## 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] Sentence [SEP] Flattened table [SEP]
```

### Pre-training

The model was pre-trained on 32 Cloud TPU v3 cores for 1,000,000 steps with maximum sequence length 512 and batch size of 512.
In this setup, pre-training on MLM only takes around 3 days. Aditionally, the model has been further pre-trained on a second task (table entailment). See the original TAPAS [paper](https://www.aclweb.org/anthology/2020.acl-main.398/) and the [follow-up paper](https://www.aclweb.org/anthology/2020.findings-emnlp.27/) for more details.

The optimizer used is Adam with a learning rate of 5e-5, and a warmup
ratio of 0.01.

### BibTeX entry and citation info

```bibtex
@misc{herzig2020tapas,
      title={TAPAS: Weakly Supervised Table Parsing via Pre-training},
      author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller 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 Müller},
      year={2020},
      eprint={2010.00571},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


---
language: en
tags:
-   multiberts
-   multiberts-seed_3
-   multiberts-seed_3-step_900k
license: apache-2.0
---

# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 900k

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 900k (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_900k')
model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_900k")
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_900k')
model = BertModel.from_pretrained("google/multiberts-seed_3-step_900k")
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}
}
```


---
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}
}
```


---
language: en
tags:
-   multiberts
-   multiberts-seed_4
-   multiberts-seed_4-step_500k
license: apache-2.0
---

# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 500k

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 500k (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_500k')
model = TFBertModel.from_pretrained("google/multiberts-seed_4-step_500k")
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_500k')
model = BertModel.from_pretrained("google/multiberts-seed_4-step_500k")
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}
}
```


---
language: multilingual
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---

# CANINE-s (CANINE pre-trained with subword loss)

Pretrained CANINE model on 104 languages using a masked language modeling (MLM) objective. It was introduced in the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) and first released in [this repository](https://github.com/google-research/language/tree/master/language/canine).

What's special about CANINE is that it doesn't require an explicit tokenizer (such as WordPiece or SentencePiece) as other models like BERT and RoBERTa. Instead, it directly operates at a character level: each character is turned into its [Unicode code point](https://en.wikipedia.org/wiki/Code_point#:~:text=For%20Unicode%2C%20the%20particular%20sequence,forming%20a%20self%2Dsynchronizing%20code.).

This means that input processing is trivial and can typically be accomplished as:

```
input_ids = [ord(char) for char in text]
```

The ord() function is part of Python, and turns each character into its Unicode code point.

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

## Model description

CANINE is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion, similar to BERT. This means it was pretrained on the raw 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): one randomly masks part of the inputs, which the model needs to predict. This model (CANINE-s) is trained with a subword loss, meaning that the model needs to predict the identities of subword tokens, while taking characters as input. By reading characters yet predicting subword tokens, the hard token boundary constraint found in other models such as BERT is turned into a soft inductive bias in CANINE.
* Next sentence prediction (NSP): the model concatenates two sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not.

This way, the model learns an inner representation of multiple languages that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the CANINE model as inputs.

## Intended uses & limitations

You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=canine) to look for fine-tuned versions on a task that interests you.

Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at models like GPT2.

### How to use

Here is how to use this model:

```python
from transformers import CanineTokenizer, CanineModel

model = CanineModel.from_pretrained('google/canine-s')
tokenizer = CanineTokenizer.from_pretrained('google/canine-s')

inputs = ["Life is like a box of chocolates.", "You never know what you gonna get."]
encoding = tokenizer(inputs, padding="longest", truncation=True, return_tensors="pt")

outputs = model(**encoding) # forward pass
pooled_output = outputs.pooler_output
sequence_output = outputs.last_hidden_state
```

## Training data

The CANINE model was pretrained on on the multilingual Wikipedia data of [mBERT](https://github.com/google-research/bert/blob/master/multilingual.md), which includes 104 languages.

### BibTeX entry and citation info

```bibtex
@article{DBLP:journals/corr/abs-2103-06874,
  author    = {Jonathan H. Clark and
               Dan Garrette and
               Iulia Turc and
               John Wieting},
  title     = {{CANINE:} Pre-training an Efficient Tokenization-Free Encoder for
               Language Representation},
  journal   = {CoRR},
  volume    = {abs/2103.06874},
  year      = {2021},
  url       = {https://arxiv.org/abs/2103.06874},
  archivePrefix = {arXiv},
  eprint    = {2103.06874},
  timestamp = {Tue, 16 Mar 2021 11:26:59 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2103-06874.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```


---
language: multilingual
datasets:
- mc4

license: apache-2.0
---

# ByT5 - xl

ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-xl).

ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.

ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-xl` significantly outperforms [mt5-xl](https://huggingface.co/google/mt5-xl) on [TweetQA](https://arxiv.org/abs/1907.06292).

Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626)

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

## Example Inference

ByT5 works on raw UTF-8 bytes and can be used without a tokenizer:

```python
from transformers import T5ForConditionalGeneration
import torch

model = T5ForConditionalGeneration.from_pretrained('google/byt5-xl')

input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3  # add 3 for special tokens
labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3  # add 3 for special tokens

loss = model(input_ids, labels=labels).loss # forward pass
```

For batched inference & training it is however recommended using a tokenizer class for padding:

```python
from transformers import T5ForConditionalGeneration, AutoTokenizer

model = T5ForConditionalGeneration.from_pretrained('google/byt5-xl')
tokenizer = AutoTokenizer.from_pretrained('google/byt5-xl')

model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt")
labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids

loss = model(**model_inputs, labels=labels).loss # forward pass
```

## Abstract

Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments.

![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/ByT5.png)


---
language: en
tags:
- fnet
license: apache-2.0
datasets:
- c4
---

# FNet base model

Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was
introduced in [this paper](https://arxiv.org/abs/2105.03824) and first released in [this repository](https://github.com/google-research/google-research/tree/master/f_net).
This model is cased: it makes a difference between english and English. The model achieves 0.58 accuracy on MLM objective and 0.80 on NSP objective.

Disclaimer: This model card has been written by [gchhablani](https://huggingface.co/gchhablani).

## Model description

FNet is a transformers model with attention replaced with fourier transforms. Hence, the inputs do not contain an `attention_mask`. It is pretrained on a large corpus of
English data in a self-supervised fashion. This means it was pretrained on the raw 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 sentence, the model randomly masks 15% of the words in the input then run
  the entire masked sentence through the model and 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 the
  sentence.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
  predict if the two sentences were following each other or not.

This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the FNet model as inputs.

## Intended uses & limitations

You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=fnet) to look for
fine-tuned versions on a task that interests you.

Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.

## Training data

The FNet model was pretrained on [C4](https://huggingface.co/datasets/c4), a cleaned version of the Common Crawl dataset.

## Training procedure

### Preprocessing

The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 32,000. The inputs of the model are
then of the form:

```
[CLS] Sentence A [SEP] Sentence B [SEP]
```

With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.

The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.

### Pretraining

FNet-base was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
of 256. The sequence length was limited to 512 tokens. The optimizer
used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.

## Evaluation results

FNet-base was fine-tuned and evaluated on the validation data of the [GLUE benchamrk](https://huggingface.co/datasets/glue). The results of the official model (written in Flax) can be seen in Table 1 on page 7 of [the official paper](https://arxiv.org/abs/2105.03824).

For comparison, this model (ported to PyTorch) was fine-tuned and evaluated using the [official Hugging Face GLUE evaluation scripts](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification#glue-tasks) alongside [bert-base-cased](https://hf.co/models/bert-base-cased) for comparison.
The training was done on a single 16GB NVIDIA Tesla V100 GPU. For MRPC/WNLI, the models were trained for 5 epochs, while for other tasks, the models were trained for 3 epochs. A sequence length of 512 was used with batch size 16 and learning rate 2e-5.

The following table summarizes the results for [fnet-base](https://huggingface.co/google/fnet-base) (called *FNet (PyTorch) - Reproduced*) and [bert-base-cased](https://hf.co/models/bert-base-cased) (called *Bert (PyTorch) - Reproduced*) in terms of **fine-tuning** speed. The format is *hour:min:seconds*. **Note** that the authors compared **pre-traning** speed in [the official paper](https://arxiv.org/abs/2105.03824) instead.

| Task/Model | FNet-base (PyTorch) |Bert-base (PyTorch)|
|:----:|:-----------:|:----:|
| MNLI-(m/mm) | [06:40:55](https://huggingface.co/gchhablani/fnet-base-finetuned-mnli) | [09:52:33](https://huggingface.co/gchhablani/bert-base-cased-finetuned-mnli)|
| QQP  | [06:21:16](https://huggingface.co/gchhablani/fnet-base-finetuned-qqp) | [09:25:01](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qqp) |
| QNLI  | [01:48:22](https://huggingface.co/gchhablani/fnet-base-finetuned-qnli)  | [02:40:22](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qnli)|
| SST-2 | [01:09:27](https://huggingface.co/gchhablani/fnet-base-finetuned-sst2) | [01:42:17](https://huggingface.co/gchhablani/bert-base-cased-finetuned-sst2)|
| CoLA  | [00:09:47](https://huggingface.co/gchhablani/fnet-base-finetuned-cola) | [00:14:20](https://huggingface.co/gchhablani/bert-base-cased-finetuned-cola)|
| STS-B | [00:07:09](https://huggingface.co/gchhablani/fnet-base-finetuned-stsb) | [00:10:24](https://huggingface.co/gchhablani/bert-base-cased-finetuned-stsb)|
| MRPC  | [00:07:48](https://huggingface.co/gchhablani/fnet-base-finetuned-mrpc) | [00:11:12](https://huggingface.co/gchhablani/bert-base-cased-finetuned-mrpc)|
| RTE  | [00:03:24](https://huggingface.co/gchhablani/fnet-base-finetuned-rte) | [00:04:51](https://huggingface.co/gchhablani/bert-base-cased-finetuned-rte)|
| WNLI | [00:02:37](https://huggingface.co/gchhablani/fnet-base-finetuned-wnli) | [00:03:23](https://huggingface.co/gchhablani/bert-base-cased-finetuned-wnli)|
| SUM | 16:30:45 | 24:23:56 |

On average the PyTorch version of FNet-base requires *ca.* 32% less time for GLUE fine-tuning on GPU.

The following table summarizes the results for [fnet-base](https://huggingface.co/google/fnet-base) (called *FNet (PyTorch) - Reproduced*) and [bert-base-cased](https://hf.co/models/bert-base-cased) (called *Bert (PyTorch) - Reproduced*) in terms of performance and compares it to the reported performance of the official FNet-base model (called *FNet (Flax) - Official*). Note that the training hyperparameters of the reproduced models were not the same as the official model, so the performance may differ significantly for some tasks (for example: CoLA).

| Task/Model | Metric | FNet-base (PyTorch) | Bert-base (PyTorch) | FNet-Base (Flax - official) |
|:----:|:-----------:|:----:|:-----------:|:----:|
| MNLI-(m/mm) | Accuracy or Match/Mismatch  | [76.75](https://huggingface.co/gchhablani/fnet-base-finetuned-mnli) | [84.10](https://huggingface.co/gchhablani/bert-base-cased-finetuned-mnli) | 72/73 |
| QQP | mean(Accuracy,F1)  | [86.5](https://huggingface.co/gchhablani/fnet-base-finetuned-qqp) | [89.26](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qqp) | 83 |
| QNLI | Accuracy  | [84.39](https://huggingface.co/gchhablani/fnet-base-finetuned-qnli) | [90.99](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qnli) | 80 |
| SST-2 | Accuracy  | [89.45](https://huggingface.co/gchhablani/fnet-base-finetuned-sst2) | [92.32](https://huggingface.co/gchhablani/bert-base-cased-finetuned-sst2) | 95 |
| CoLA | Matthews corr or Accuracy | [35.94](https://huggingface.co/gchhablani/fnet-base-finetuned-cola) | [59.57](https://huggingface.co/gchhablani/bert-base-cased-finetuned-cola) | 69 |
| STS-B | Spearman corr. | [82.19](https://huggingface.co/gchhablani/fnet-base-finetuned-stsb) | [88.98](https://huggingface.co/gchhablani/bert-base-cased-finetuned-stsb) | 79 |
| MRPC | mean(F1/Accuracy) | [81.15](https://huggingface.co/gchhablani/fnet-base-finetuned-mrpc) | [88.15](https://huggingface.co/gchhablani/bert-base-cased-finetuned-mrpc) | 76 |
| RTE | Accuracy | [62.82](https://huggingface.co/gchhablani/fnet-base-finetuned-rte) | [67.15](https://huggingface.co/gchhablani/bert-base-cased-finetuned-rte) | 63 |
| WNLI | Accuracy | [54.93](https://huggingface.co/gchhablani/fnet-base-finetuned-wnli) | [46.48](https://huggingface.co/gchhablani/bert-base-cased-finetuned-wnli) | - |
| Avg  | - | 72.7 | 78.6 | 76.7 |

We can see that FNet-base achieves around 93% of BERT-base's performance on average.

For more details, please refer to the checkpoints linked with the scores. On overview of all fine-tuned checkpoints of the following table can be accessed [here](https://huggingface.co/models?other=fnet-bert-base-comparison).

### How to use

You can use this model directly with a pipeline for masked language modeling:

**Note: The mask filling pipeline doesn't work exactly as the original model performs masking after converting to tokens. In masking pipeline an additional space is added after the [MASK].**

```python
>>> from transformers import FNetForMaskedLM, FNetTokenizer, pipeline
>>> tokenizer = FNetTokenizer.from_pretrained("google/fnet-base")
>>> model = FNetForMaskedLM.from_pretrained("google/fnet-base")
>>> unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
>>> unmasker("Hello I'm a [MASK] model.")

[
    {"sequence": "hello i'm a new model.", "score": 0.12073223292827606, "token": 351, "token_str": "new"},
    {"sequence": "hello i'm a first model.", "score": 0.08501081168651581, "token": 478, "token_str": "first"},
    {"sequence": "hello i'm a next model.", "score": 0.060546260327100754, "token": 1037, "token_str": "next"},
    {"sequence": "hello i'm a last model.", "score": 0.038265593349933624, "token": 813, "token_str": "last"},
    {"sequence": "hello i'm a sister model.", "score": 0.033868927508592606, "token": 6232, "token_str": "sister"},
]

```

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

**Note: You must specify the maximum sequence length to be 512 and truncate/pad to the same length because the original model has no attention mask and considers all the hidden states during forward pass.**

```python
from transformers import FNetTokenizer, FNetModel
tokenizer = FNetTokenizer.from_pretrained("google/fnet-base")
model = FNetModel.from_pretrained("google/fnet-base")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt', padding='max_length', truncation=True, max_length=512)
output = model(**encoded_input)
```

### BibTeX entry and citation info

```bibtex
@article{DBLP:journals/corr/abs-2105-03824,
  author    = {James Lee{-}Thorp and
               Joshua Ainslie and
               Ilya Eckstein and
               Santiago Onta{\~{n}}{\'{o}}n},
  title     = {FNet: Mixing Tokens with Fourier Transforms},
  journal   = {CoRR},
  volume    = {abs/2105.03824},
  year      = {2021},
  url       = {https://arxiv.org/abs/2105.03824},
  archivePrefix = {arXiv},
  eprint    = {2105.03824},
  timestamp = {Fri, 14 May 2021 12:13:30 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2105-03824.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```

## Contributions
Thanks to [@gchhablani](https://huggingface.co/gchhablani) for adding this model.


---
language: en
tags:
-   multiberts
-   multiberts-seed_15
license: apache-2.0
---

# MultiBERTs - Seed 15

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 #15.

## 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_15')
model = TFBertModel.from_pretrained("google/multiberts-seed_15")
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_15')
model = BertModel.from_pretrained("google/multiberts-seed_15")
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}
}
```


---
language: en
tags:
- tapas
license: apache-2.0
datasets:
- wikisql
---

# TAPAS small model fine-tuned on WikiSQL (in a supervised fashion)

his model has 2 versions which can be used. The default version corresponds to the `tapas_wikisql_sqa_inter_masklm_small_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), and [WikiSQL](https://github.com/salesforce/WikiSQL). 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_wikisql_sqa_inter_masklm_small` (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.

## 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 and aggregation head on top of the pre-trained model, and then jointly train these randomly initialized classification heads with the base model on SQA and WikiSQL.

## Intended uses & limitations

You can use this model for answering questions related to a table.

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]
```

The authors did first convert the WikiSQL dataset into the format of SQA using automatic conversion scripts.

### Fine-tuning

The model was fine-tuned on 32 Cloud TPU v3 cores for 50,000 steps with maximum sequence length 512 and batch size of 512.
In this setup, fine-tuning takes around 10 hours. The optimizer used is Adam with a learning rate of 6.17164e-5, and a warmup
ratio of 0.1424. See the [paper](https://arxiv.org/abs/2004.02349) for more details (tables 11 and 12).

### BibTeX entry and citation info

```bibtex
@misc{herzig2020tapas,
      title={TAPAS: Weakly Supervised Table Parsing via Pre-training},
      author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller 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 Müller},
      year={2020},
      eprint={2010.00571},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

```bibtex
@article{DBLP:journals/corr/abs-1709-00103,
  author    = {Victor Zhong and
               Caiming Xiong and
               Richard Socher},
  title     = {Seq2SQL: Generating Structured Queries from Natural Language using
               Reinforcement Learning},
  journal   = {CoRR},
  volume    = {abs/1709.00103},
  year      = {2017},
  url       = {http://arxiv.org/abs/1709.00103},
  archivePrefix = {arXiv},
  eprint    = {1709.00103},
  timestamp = {Mon, 13 Aug 2018 16:48:41 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1709-00103.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```


---
language: en
tags:
-   multiberts
-   multiberts-seed_3
-   multiberts-seed_3-step_140k
license: apache-2.0
---

# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 140k

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 140k (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_140k')
model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_140k")
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_140k')
model = BertModel.from_pretrained("google/multiberts-seed_3-step_140k")
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}
}
```


---
language: en
tags:
-   multiberts
-   multiberts-seed_1
-   multiberts-seed_1-step_80k
license: apache-2.0
---

# MultiBERTs, Intermediate Checkpoint - Seed 1, 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 #1, captured at step 80k (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_80k')
model = TFBertModel.from_pretrained("google/multiberts-seed_1-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_1-step_80k')
model = BertModel.from_pretrained("google/multiberts-seed_1-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}
}
```


---
language: en
tags:
-   multiberts
-   multiberts-seed_3
-   multiberts-seed_3-step_20k
license: apache-2.0
---

# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 20k

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 20k (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_20k')
model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_20k")
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_20k')
model = BertModel.from_pretrained("google/multiberts-seed_3-step_20k")
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}
}
```


## Reformer Language model on character level and trained on enwik8.

*enwik8* is a dataset based on Wikipedia and is often used to measure the model's ability to *compress* data, *e.g.* in
the scope of the *Hutter prize*: https://en.wikipedia.org/wiki/Hutter_Prize.

`reformer-enwik8` was pretrained on the first 90M chars of *enwik8* whereas the text was chunked into batches of size 65536 chars (=2^16).
The model's weights were taken from https://console.cloud.google.com/storage/browser/trax-ml/reformer/enwik8 and converted
to Hugging Face's PyTorch ReformerLM model `ReformerModelWithLMHead`.

The model is a language model that operates on characters.
Therefore, this model does not need a tokenizer. The following function can instead be used for **encoding** and **decoding**:

```python
import torch

# Encoding
def encode(list_of_strings, pad_token_id=0):
    max_length = max([len(string) for string in list_of_strings])

    # create emtpy tensors
    attention_masks = torch.zeros((len(list_of_strings), max_length), dtype=torch.long)
    input_ids = torch.full((len(list_of_strings), max_length), pad_token_id, dtype=torch.long)

    for idx, string in enumerate(list_of_strings):
        # make sure string is in byte format
        if not isinstance(string, bytes):
            string = str.encode(string)

        input_ids[idx, :len(string)] = torch.tensor([x + 2 for x in string])
        attention_masks[idx, :len(string)] = 1

    return input_ids, attention_masks

# Decoding
def decode(outputs_ids):
    decoded_outputs = []
    for output_ids in outputs_ids.tolist():
        # transform id back to char IDs < 2 are simply transformed to ""
        decoded_outputs.append("".join([chr(x - 2) if x > 1 else "" for x in output_ids]))
    return decoded_outputs
```

Text can be generated as follows:

```python
from transformers import ReformerModelWithLMHead

model = ReformerModelWithLMHead.from_pretrained("google/reformer-enwik8")
encoded, attention_masks = encode(["In 1965, Brooks left IBM to found the Department of"])
decode(model.generate(encoded, do_sample=True, max_length=150))

# gives:
# In 1965, Brooks left IBM to found the Department of Journalism in 1968. IBM had jurisdiction himself in 1980, while Brooks resolved, nevertheless thro

```

***Note***: Language generation using `ReformerModelWithLMHead` is not optimized yet and is rather slow.