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


## Intended uses & limitations


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


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


## Training procedure


### Preprocessing


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


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


### Fine-tuning


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


### BibTeX entry and citation info


```bibtex
@misc{herzig2020tapas,
      title={TAPAS: Weakly Supervised Table Parsing via Pre-training},
      author={Jonathan Herzig and PaweÃ…â€š Krzysztof Nowak and Thomas MÃƒÂ¼ller 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 MÃƒÂ¼ller},
      year={2020},
      eprint={2010.00571},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


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


model name: tapas-mini-finetuned-sqa


model author: google


task: table-question-answering


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


language: en
tags:


- multiberts
- multiberts-seed_14
license: apache-2.0


# MultiBERTs - Seed 14


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 #14.


## 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_14')
model = TFBertModel.from_pretrained("google/multiberts-seed_14")
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_14')
model = BertModel.from_pretrained("google/multiberts-seed_14")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


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


model name: multiberts-seed_14


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_14, license:apache-2.0, infinity_compatible


language: en
tags:


- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_2000k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 2000k


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


## Model Description


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


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


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


### How to use


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_2000k')
model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_2000k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


PyTorch version:


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_2000k')
model = BertModel.from_pretrained("google/multiberts-seed_3-step_2000k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


model name: multiberts-seed_3-step_2000k


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_3, multiberts-seed_3-step_2000k, license:apache-2.0, infinity_compatible


license: apache-2.0
tags:


- image-classification
- vision
datasets:
- imagenet
- imagenet-21k


# Vision Transformer (large-sized model)


Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.


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


## Model description


The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.


Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.


By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.


## Intended uses & limitations


You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for
fine-tuned versions on a task that interests you.


### How to use


Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:


```python
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-large-patch16-384')
model = ViTForImageClassification.from_pretrained('google/vit-large-patch16-384')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```


Currently, both the feature extractor and model  support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.


## Training data


The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.


## Training procedure


### Preprocessing


The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).


Images are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).


### Pretraining


The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.


## Evaluation results


For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.


### BibTeX entry and citation info


```bibtex
@misc{wu2020visual,
      title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
      author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
      year={2020},
      eprint={2006.03677},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```


```bibtex
@inproceedings{deng2009imagenet,
  title={Imagenet: A large-scale hierarchical image database},
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  booktitle={2009 IEEE conference on computer vision and pattern recognition},
  pages={248--255},
  year={2009},
  organization={Ieee}
}
```


model name: vit-large-patch16-384


model author: google


task: image-classification


tags: pytorch, tf, jax, vit, image-classification, dataset:imagenet, dataset:imagenet-21k, arxiv:2010.11929, arxiv:2006.03677, transformers, license:apache-2.0, vision


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


## license: apache-2.0


# BERT Miniatures


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


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


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


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


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


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


Here are the corresponding GLUE scores on the test set:


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


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


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


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


model name: bert_uncased_L-4_H-128_A-2


model author: google


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


language: en
tags:


- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_1600k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 2, 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 #2, 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_2-step_1600k')
model = TFBertModel.from_pretrained("google/multiberts-seed_2-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_2-step_1600k')
model = BertModel.from_pretrained("google/multiberts-seed_2-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}
}
```


model name: multiberts-seed_2-step_1600k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_2, multiberts-seed_2-step_1600k, license:apache-2.0, infinity_compatible


language: en
tags:


- multiberts
- multiberts-seed_0
- multiberts-seed_0-step_1900k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1900k


This is model #0, captured at step 1900k (max: 2000k, i.e., 2M steps).


## Model Description


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_1900k')
model = TFBertModel.from_pretrained("google/multiberts-seed_0-step_1900k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_1900k')
model = BertModel.from_pretrained("google/multiberts-seed_0-step_1900k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


model name: multiberts-seed_0-step_1900k


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_0, multiberts-seed_0-step_1900k, license:apache-2.0, infinity_compatible


- tapas
- table-question-answering
license: apache-2.0
datasets:
- wtq


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


This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_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), [WikiSQL](https://github.com/salesforce/WikiSQL) and finally [WTQ](https://github.com/ppasupat/WikiTableQuestions). It uses relative position embeddings (i.e. resetting the position index at every cell of the table).


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


- `no_reset`, which corresponds to `tapas_wtq_wikisql_sqa_inter_masklm_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.


## Results


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


## Model description


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


- 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, WikiSQL and finally WTQ.


## 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 WTQ 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 1.93581e-5, and a warmup
ratio of 0.128960. An inductive bias is added such that the model only selects cells of the same column. This is reflected by the
`select_one_column` parameter of `TapasConfig`. See 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/PasupatL15,
  author    = {Panupong Pasupat and
               Percy Liang},
  title     = {Compositional Semantic Parsing on Semi-Structured Tables},
  journal   = {CoRR},
  volume    = {abs/1508.00305},
  year      = {2015},
  url       = {http://arxiv.org/abs/1508.00305},
  archivePrefix = {arXiv},
  eprint    = {1508.00305},
  timestamp = {Mon, 13 Aug 2018 16:47:37 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/PasupatL15.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```


model name: tapas-small-finetuned-wtq


model author: google


task: table-question-answering


tags: pytorch, tf, tapas, table-question-answering, en, dataset:wtq, arxiv:2004.02349, arxiv:2010.00571, arxiv:1508.00305, transformers, license:apache-2.0


This model corresponds to **tapas_masklm_tiny_reset** of the [original repository](https://github.com/google-research/tapas).


Here's how you can use it:


```python
from transformers import TapasTokenizer, TapasForMaskedLM
import pandas as pd
import torch

tokenizer = TapasTokenizer.from_pretrained("google/tapas-tiny-masklm")
model = TapasForMaskedLM.from_pretrained("google/tapas-tiny-masklm")

data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
        'Age': ["56", "45", "59"],
        'Number of movies': ["87", "53", "69"]
}
table = pd.DataFrame.from_dict(data)
query = "How many movies has Leonardo [MASK] Caprio played in?"

# prepare inputs
inputs = tokenizer(table=table, queries=query, padding="max_length", return_tensors="pt")

# forward pass
outputs = model(**inputs)

# return top 5 values and predictions
masked_index = torch.nonzero(inputs.input_ids.squeeze() == tokenizer.mask_token_id, as_tuple=False)
logits = outputs.logits[0, masked_index.item(), :]
probs = logits.softmax(dim=0)
values, predictions = probs.topk(5)

for value, pred in zip(values, predictions):
  print(f"{tokenizer.decode([pred])} with confidence {value}")
```


model name: tapas-tiny-masklm


task: fill-mask


tags: pytorch, tf, tapas, fill-mask, transformers, autonlp_compatible


language: en
tags:


- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_1200k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 1200k


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 1200k (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_1200k')
model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_1200k")
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_1200k')
model = BertModel.from_pretrained("google/multiberts-seed_3-step_1200k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


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


model name: multiberts-seed_3-step_1200k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_3, multiberts-seed_3-step_1200k, license:apache-2.0, infinity_compatible


language: en
tags:


- summarization


### Pegasus Models


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


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


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


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


Task: Summarization


The following is copied from the authors' README.


# Mixed & Stochastic Checkpoints


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


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


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


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


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


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


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


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


Citation


```

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


model name: pegasus-cnn_dailymail


model author: google


task: summarization


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


language: en
tags:


- multiberts
- multiberts-seed_21
license: apache-2.0


# MultiBERTs - Seed 21


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 #21.


## 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_21')
model = TFBertModel.from_pretrained("google/multiberts-seed_21")
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_21')
model = BertModel.from_pretrained("google/multiberts-seed_21")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


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


model name: multiberts-seed_21


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_21, license:apache-2.0, infinity_compatible


language: en
tags:


- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_200k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 200k


This is model #2, captured at step 200k (max: 2000k, i.e., 2M steps).


## Model Description


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


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


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


### How to use


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_200k')
model = TFBertModel.from_pretrained("google/multiberts-seed_2-step_200k")
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_200k')
model = BertModel.from_pretrained("google/multiberts-seed_2-step_200k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


model name: multiberts-seed_2-step_200k


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_2, multiberts-seed_2-step_200k, license:apache-2.0, infinity_compatible


- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_100k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 100k


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 100k (max: 2000k, i.e., 2M steps).


## Model Description


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


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


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_100k')
model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_100k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_100k')
model = BertModel.from_pretrained("google/multiberts-seed_3-step_100k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


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


model name: multiberts-seed_3-step_100k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_3, multiberts-seed_3-step_100k, license:apache-2.0, infinity_compatible


language: en
license: apache-2.0
datasets:


- xsum
tags:
- summarization


# Roberta2Roberta_L-24_bbc EncoderDecoder model


The model was introduced in
[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/roberta24_bbc/1).


The model is an encoder-decoder model that was initialized on the `roberta-large` checkpoints for both the encoder
and decoder and fine-tuned on extreme summarization on the BBC XSum dataset, which is linked above.


Disclaimer: The model card has been written by the Hugging Face team.


## How to use


You can use this model for extreme summarization, *e.g.*


```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_bbc")
model = AutoModelForSeq2SeqLM.from_pretrained("google/roberta2roberta_L-24_bbc")

article = """The problem is affecting people using the older
versions of the PlayStation 3, called the "Fat"
model.The problem isn't affecting the newer PS3
Slim systems that have been on sale since
September last year.Sony have also said they are
aiming to have the problem fixed shortly but is
advising some users to avoid using their console
for the time being."We hope to resolve this
problem within the next 24 hours," a statement
reads. "In the meantime, if you have a model other
than the new slim PS3, we advise that you do not
use your PS3 system, as doing so may result in
errors in some functionality, such as recording
obtained trophies, and not being able to restore
certain data."We believe we have identified that
this problem is being caused by a bug in the clock
functionality incorporated in the system."The
PlayStation Network is used by millions of people
around the world.It allows users to play their
friends at games like Fifa over the internet and
also do things like download software or visit
online stores."""

input_ids = tokenizer(article, return_tensors="pt").input_ids
output_ids = model.generate(input_ids)[0]
print(tokenizer.decode(output_ids, skip_special_tokens=True))
# should output
# Some Sony PlayStation gamers are being advised to stay away from the network because of a problem with the PlayStation 3 network.
```


model name: roberta2roberta_L-24_bbc


task: summarization


tags: pytorch, encoder-decoder, text2text-generation, en, dataset:xsum, arxiv:1907.12461, transformers, license:apache-2.0, summarization, autonlp_compatible


language: en
tags:


- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_500k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 3, 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 #3, 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_3-step_500k')
model = TFBertModel.from_pretrained("google/multiberts-seed_3-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_3-step_500k')
model = BertModel.from_pretrained("google/multiberts-seed_3-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}
}
```


model name: multiberts-seed_3-step_500k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_3, multiberts-seed_3-step_500k, license:apache-2.0, infinity_compatible


language: en
datasets:


- c4
- wikipedia
- natural_questions


## license: apache-2.0


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


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


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


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


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


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


## Results on Natural Questions - Test Set


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


## Usage


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


```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

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

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

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


## Abstract


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


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


model name: t5-xxl-ssm-nqo


model author: google


task: text2text-generation


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


language: en
tags:


- multiberts
- multiberts-seed_0
- multiberts-seed_0-step_40k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 40k


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 40k (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_40k')
model = TFBertModel.from_pretrained("google/multiberts-seed_0-step_40k")
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_40k')
model = BertModel.from_pretrained("google/multiberts-seed_0-step_40k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


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


model name: multiberts-seed_0-step_40k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_0, multiberts-seed_0-step_40k, license:apache-2.0, infinity_compatible


language: en
tags:


- tapas
- sequence-classification
license: apache-2.0
datasets:
- tab_fact


# TAPAS mini 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_mini_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_mini`


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}
}
```


model name: tapas-mini-finetuned-tabfact


model author: google


task: text-classification


tags: pytorch, tf, tapas, text-classification, en, dataset:tab_fact, arxiv:2010.00571, arxiv:2004.02349, transformers, sequence-classification, license:apache-2.0


license: apache-2.0
tags:


- vision
datasets:
- imagenet-21k
inference: false


# Vision Transformer (large-sized model)


Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.


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


## Model description


The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.


Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.


Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).


By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.


## Intended uses & limitations


You can use the raw model to embed images, but it's mostly intended to be fine-tuned on a downstream task.


### How to use


Here is how to use this model:


```python
from transformers import ViTFeatureExtractor, ViTModel
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-large-patch16-224-in21k')
model = ViTModel.from_pretrained('google/vit-large-patch16-224-in21k')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_state = outputs.last_hidden_state
```


Currently, both the feature extractor and model  support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.


## Training data


The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes.


## Training procedure


### Preprocessing


The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).


Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).


### Pretraining


The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.


## Evaluation results


For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.


### BibTeX entry and citation info


```bibtex
@misc{wu2020visual,
      title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
      author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
      year={2020},
      eprint={2006.03677},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```


```bibtex
@inproceedings{deng2009imagenet,
  title={Imagenet: A large-scale hierarchical image database},
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  booktitle={2009 IEEE conference on computer vision and pattern recognition},
  pages={248--255},
  year={2009},
  organization={Ieee}
}
```


model name: vit-large-patch16-224-in21k


model author: google


task: feature-extraction


tags: pytorch, tf, jax, vit, feature-extraction, dataset:imagenet-21k, arxiv:2010.11929, arxiv:2006.03677, transformers, license:apache-2.0, vision


language: en
tags:


- multiberts
- multiberts-seed_1
- multiberts-seed_1-step_40k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 40k


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 40k (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_40k')
model = TFBertModel.from_pretrained("google/multiberts-seed_1-step_40k")
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_40k')
model = BertModel.from_pretrained("google/multiberts-seed_1-step_40k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


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


model name: multiberts-seed_1-step_40k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_1, multiberts-seed_1-step_40k, license:apache-2.0, infinity_compatible


language: en
datasets:


- c4
tags:
- t5-lm-adapt


## license: apache-2.0


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


## Version 1.1 - LM-Adapted


[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) includes the following improvements compared to the original [T5 model](https://huggingface.co/t5-3b):


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


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


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


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


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


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


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


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


## Abstract


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


model name: t5-xl-lm-adapt


model author: google


task: text2text-generation


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


language: en
tags:


- multiberts
- multiberts-seed_0
- multiberts-seed_0-step_900k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 0, 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 #0, 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_0-step_900k')
model = TFBertModel.from_pretrained("google/multiberts-seed_0-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_0-step_900k')
model = BertModel.from_pretrained("google/multiberts-seed_0-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}
}
```


model name: multiberts-seed_0-step_900k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_0, multiberts-seed_0-step_900k, license:apache-2.0, infinity_compatible


language: en
datasets:


- c4


## license: apache-2.0


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


## Version 1.1


[T5 Version 1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md#t511) includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202).


- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.
- Pre-trained on C4 only without mixing in the downstream tasks.
- no parameter sharing between embedding and classifier layer
- "xl" and "xxl" replace "3B" and "11B". The model shapes are a bit different - larger `d_model` and smaller `num_heads` and `d_ff`.


**Note**: T5 Version 1.1 was only pre-trained on C4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
Pretraining Dataset: [C4](https://huggingface.co/datasets/c4)


Other Community Checkpoints: [here](https://huggingface.co/models?search=t5-v1_1)


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


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


## Abstract


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


model name: t5-v1_1-xxl


task: text2text-generation


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


language: en
tags:


- multiberts
- multiberts-seed_1
- multiberts-seed_1-step_1400k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1400k


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 1400k (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_1400k')
model = TFBertModel.from_pretrained("google/multiberts-seed_1-step_1400k")
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_1400k')
model = BertModel.from_pretrained("google/multiberts-seed_1-step_1400k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


## Citation info


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


model name: multiberts-seed_1-step_1400k


model author: google


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_1, multiberts-seed_1-step_1400k, license:apache-2.0, infinity_compatible


language: en
tags:


- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_1100k
license: apache-2.0


# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 1100k


This is model #3, captured at step 1100k (max: 2000k, i.e., 2M steps).


## Model Description


```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_1100k')
model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_1100k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_1100k')
model = BertModel.from_pretrained("google/multiberts-seed_3-step_1100k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```


model name: multiberts-seed_3-step_1100k


tags: pytorch, tf, bert, pretraining, en, arxiv:2106.16163, arxiv:1908.08962, transformers, multiberts, multiberts-seed_3, multiberts-seed_3-step_1100k, license:apache-2.0, infinity_compatible


language: en
datasets:


- c4
tags:
- t5-lm-adapt


## license: apache-2.0


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


## Version 1.1 - LM-Adapted


[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) includes the following improvements compared to the original [T5 model](https://huggingface.co/t5-11b):


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


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


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


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


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


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


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


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


## Abstract


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


model name: t5-xxl-lm-adapt


model author: google


task: text2text-generation


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


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


## license: apache-2.0


# BERT Miniatures


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


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


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


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


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


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


Here are the corresponding GLUE scores on the test set:


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


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


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


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


model name: bert_uncased_L-8_H-256_A-4


model author: google


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


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).