|
--- |
|
language: en |
|
license: apache-2.0 |
|
datasets: |
|
- bookcorpus |
|
- wikipedia |
|
--- |
|
|
|
# ALBERT Large v1 |
|
|
|
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in |
|
[this paper](https://arxiv.org/abs/1909.11942) and first released in |
|
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a difference |
|
between english and English. |
|
|
|
Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by |
|
the Hugging Face team. |
|
|
|
## Model description |
|
|
|
ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it |
|
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of |
|
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it |
|
was pretrained with two objectives: |
|
|
|
- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run |
|
the entire masked sentence through the model and has to predict the masked words. This is different from traditional |
|
recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like |
|
GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the |
|
sentence. |
|
- Sentence Ordering Prediction (SOP): ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text. |
|
|
|
This way, the model learns an inner representation of the English language that can then be used to extract features |
|
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard |
|
classifier using the features produced by the ALBERT model as inputs. |
|
|
|
ALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers. |
|
|
|
This is the first version of the large model. Version 2 is different from version 1 due to different dropout rates, additional training data, and longer training. It has better results in nearly all downstream tasks. |
|
|
|
This model has the following configuration: |
|
|
|
- 24 repeating layers |
|
- 128 embedding dimension |
|
- 1024 hidden dimension |
|
- 16 attention heads |
|
- 17M parameters |
|
|
|
## Intended uses & limitations |
|
|
|
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to |
|
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=albert) to look for |
|
fine-tuned versions on a task that interests you. |
|
|
|
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) |
|
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text |
|
generation you should look at model like GPT2. |
|
|
|
### How to use |
|
|
|
You can use this model directly with a pipeline for masked language modeling: |
|
|
|
```python |
|
>>> from transformers import pipeline |
|
>>> unmasker = pipeline('fill-mask', model='albert-large-v1') |
|
>>> unmasker("Hello I'm a [MASK] model.") |
|
[ |
|
{ |
|
"sequence":"[CLS] hello i'm a modeling model.[SEP]", |
|
"score":0.05816134437918663, |
|
"token":12807, |
|
"token_str":"â–modeling" |
|
}, |
|
{ |
|
"sequence":"[CLS] hello i'm a modelling model.[SEP]", |
|
"score":0.03748830780386925, |
|
"token":23089, |
|
"token_str":"â–modelling" |
|
}, |
|
{ |
|
"sequence":"[CLS] hello i'm a model model.[SEP]", |
|
"score":0.033725276589393616, |
|
"token":1061, |
|
"token_str":"â–model" |
|
}, |
|
{ |
|
"sequence":"[CLS] hello i'm a runway model.[SEP]", |
|
"score":0.017313428223133087, |
|
"token":8014, |
|
"token_str":"â–runway" |
|
}, |
|
{ |
|
"sequence":"[CLS] hello i'm a lingerie model.[SEP]", |
|
"score":0.014405295252799988, |
|
"token":29104, |
|
"token_str":"â–lingerie" |
|
} |
|
] |
|
``` |
|
|
|
Here is how to use this model to get the features of a given text in PyTorch: |
|
|
|
```python |
|
from transformers import AlbertTokenizer, AlbertModel |
|
tokenizer = AlbertTokenizer.from_pretrained('albert-large-v1') |
|
model = AlbertModel.from_pretrained("albert-large-v1") |
|
text = "Replace me by any text you'd like." |
|
encoded_input = tokenizer(text, return_tensors='pt') |
|
output = model(**encoded_input) |
|
``` |
|
|
|
and in TensorFlow: |
|
|
|
```python |
|
from transformers import AlbertTokenizer, TFAlbertModel |
|
tokenizer = AlbertTokenizer.from_pretrained('albert-large-v1') |
|
model = TFAlbertModel.from_pretrained("albert-large-v1") |
|
text = "Replace me by any text you'd like." |
|
encoded_input = tokenizer(text, return_tensors='tf') |
|
output = model(encoded_input) |
|
``` |
|
|
|
### Limitations and bias |
|
|
|
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased |
|
predictions: |
|
|
|
```python |
|
>>> from transformers import pipeline |
|
>>> unmasker = pipeline('fill-mask', model='albert-large-v1') |
|
>>> unmasker("The man worked as a [MASK].") |
|
|
|
[ |
|
{ |
|
"sequence":"[CLS] the man worked as a chauffeur.[SEP]", |
|
"score":0.029577180743217468, |
|
"token":28744, |
|
"token_str":"â–chauffeur" |
|
}, |
|
{ |
|
"sequence":"[CLS] the man worked as a janitor.[SEP]", |
|
"score":0.028865724802017212, |
|
"token":29477, |
|
"token_str":"â–janitor" |
|
}, |
|
{ |
|
"sequence":"[CLS] the man worked as a shoemaker.[SEP]", |
|
"score":0.02581118606030941, |
|
"token":29024, |
|
"token_str":"â–shoemaker" |
|
}, |
|
{ |
|
"sequence":"[CLS] the man worked as a blacksmith.[SEP]", |
|
"score":0.01849772222340107, |
|
"token":21238, |
|
"token_str":"â–blacksmith" |
|
}, |
|
{ |
|
"sequence":"[CLS] the man worked as a lawyer.[SEP]", |
|
"score":0.01820771023631096, |
|
"token":3672, |
|
"token_str":"â–lawyer" |
|
} |
|
] |
|
|
|
>>> unmasker("The woman worked as a [MASK].") |
|
|
|
[ |
|
{ |
|
"sequence":"[CLS] the woman worked as a receptionist.[SEP]", |
|
"score":0.04604868218302727, |
|
"token":25331, |
|
"token_str":"â–receptionist" |
|
}, |
|
{ |
|
"sequence":"[CLS] the woman worked as a janitor.[SEP]", |
|
"score":0.028220869600772858, |
|
"token":29477, |
|
"token_str":"â–janitor" |
|
}, |
|
{ |
|
"sequence":"[CLS] the woman worked as a paramedic.[SEP]", |
|
"score":0.0261906236410141, |
|
"token":23386, |
|
"token_str":"â–paramedic" |
|
}, |
|
{ |
|
"sequence":"[CLS] the woman worked as a chauffeur.[SEP]", |
|
"score":0.024797942489385605, |
|
"token":28744, |
|
"token_str":"â–chauffeur" |
|
}, |
|
{ |
|
"sequence":"[CLS] the woman worked as a waitress.[SEP]", |
|
"score":0.024124596267938614, |
|
"token":13678, |
|
"token_str":"â–waitress" |
|
} |
|
] |
|
``` |
|
|
|
This bias will also affect all fine-tuned versions of this model. |
|
|
|
## Training data |
|
|
|
The ALBERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 |
|
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and |
|
headers). |
|
|
|
## Training procedure |
|
|
|
### Preprocessing |
|
|
|
The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 30,000. The inputs of the model are |
|
then of the form: |
|
|
|
``` |
|
[CLS] Sentence A [SEP] Sentence B [SEP] |
|
``` |
|
|
|
### Training |
|
|
|
The ALBERT procedure follows the BERT setup. |
|
|
|
The details of the masking procedure for each sentence are the following: |
|
- 15% of the tokens are masked. |
|
- In 80% of the cases, the masked tokens are replaced by `[MASK]`. |
|
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. |
|
- In the 10% remaining cases, the masked tokens are left as is. |
|
|
|
## Evaluation results |
|
|
|
When fine-tuned on downstream tasks, the ALBERT models achieve the following results: |
|
|
|
| | Average | SQuAD1.1 | SQuAD2.0 | MNLI | SST-2 | RACE | |
|
|----------------|----------|----------|----------|----------|----------|----------| |
|
|V2 | |
|
|ALBERT-base |82.3 |90.2/83.2 |82.1/79.3 |84.6 |92.9 |66.8 | |
|
|ALBERT-large |85.7 |91.8/85.2 |84.9/81.8 |86.5 |94.9 |75.2 | |
|
|ALBERT-xlarge |87.9 |92.9/86.4 |87.9/84.1 |87.9 |95.4 |80.7 | |
|
|ALBERT-xxlarge |90.9 |94.6/89.1 |89.8/86.9 |90.6 |96.8 |86.8 | |
|
|V1 | |
|
|ALBERT-base |80.1 |89.3/82.3 | 80.0/77.1|81.6 |90.3 | 64.0 | |
|
|ALBERT-large |82.4 |90.6/83.9 | 82.3/79.4|83.5 |91.7 | 68.5 | |
|
|ALBERT-xlarge |85.5 |92.5/86.1 | 86.1/83.1|86.4 |92.4 | 74.8 | |
|
|ALBERT-xxlarge |91.0 |94.8/89.3 | 90.2/87.4|90.8 |96.9 | 86.5 | |
|
|
|
|
|
### BibTeX entry and citation info |
|
|
|
```bibtex |
|
@article{DBLP:journals/corr/abs-1909-11942, |
|
author = {Zhenzhong Lan and |
|
Mingda Chen and |
|
Sebastian Goodman and |
|
Kevin Gimpel and |
|
Piyush Sharma and |
|
Radu Soricut}, |
|
title = {{ALBERT:} {A} Lite {BERT} for Self-supervised Learning of Language |
|
Representations}, |
|
journal = {CoRR}, |
|
volume = {abs/1909.11942}, |
|
year = {2019}, |
|
url = {http://arxiv.org/abs/1909.11942}, |
|
archivePrefix = {arXiv}, |
|
eprint = {1909.11942}, |
|
timestamp = {Fri, 27 Sep 2019 13:04:21 +0200}, |
|
biburl = {https://dblp.org/rec/journals/corr/abs-1909-11942.bib}, |
|
bibsource = {dblp computer science bibliography, https://dblp.org} |
|
} |
|
``` |
