Albert Large V1

🚀 ALBERT Large v1

A pretrained model on the English language using a masked language modeling (MLM) objective, which can extract useful features for downstream tasks.

🚀 Quick Start

This is a pretrained model on the English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model, like all ALBERT models, is uncased, meaning it does not distinguish 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.

✨ Features

• Bidirectional Representation Learning: Through Masked Language Modeling (MLM), the model can learn a bidirectional representation of sentences, which is different from traditional RNNs and autoregressive models.
• Sentence Ordering Prediction: ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text.
• Shared Layers: ALBERT shares its layers across its Transformer, resulting in a small memory footprint.

📦 Installation

This model can be used directly with the transformers library. You can install it via pip:

pip install transformers

💻 Usage Examples

Basic Usage

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

>>> 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":"▁modeling"
   },
   {
      "sequence":"[CLS] hello i'm a modelling model.[SEP]",
      "score":0.03748830780386925,
      "token":23089,
      "token_str":"▁modelling"
   },
   {
      "sequence":"[CLS] hello i'm a model model.[SEP]",
      "score":0.033725276589393616,
      "token":1061,
      "token_str":"▁model"
   },
   {
      "sequence":"[CLS] hello i'm a runway model.[SEP]",
      "score":0.017313428223133087,
      "token":8014,
      "token_str":"▁runway"
   },
   {
      "sequence":"[CLS] hello i'm a lingerie model.[SEP]",
      "score":0.014405295252799988,
      "token":29104,
      "token_str":"▁lingerie"
   }
]

Advanced Usage

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

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:

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)

📚 Documentation

Model Description

ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. It was pretrained with two objectives: Masked Language Modeling (MLM) and Sentence Ordering Prediction (SOP).

This way, the model learns an inner representation of the English language that can be used to extract features useful for downstream tasks.

ALBERT is particular in that it shares its layers across its Transformer, resulting in a small memory footprint. However, the computational cost remains similar to a BERT-like architecture with the same number of hidden 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, and it has better results in nearly all downstream tasks.

This model has the following configuration:

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 to look for fine-tuned versions on a task that interests you.

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

Limitations and Bias

Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions:

>>> 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":"▁chauffeur"
   },
   {
      "sequence":"[CLS] the man worked as a janitor.[SEP]",
      "score":0.028865724802017212,
      "token":29477,
      "token_str":"▁janitor"
   },
   {
      "sequence":"[CLS] the man worked as a shoemaker.[SEP]",
      "score":0.02581118606030941,
      "token":29024,
      "token_str":"▁shoemaker"
   },
   {
      "sequence":"[CLS] the man worked as a blacksmith.[SEP]",
      "score":0.01849772222340107,
      "token":21238,
      "token_str":"▁blacksmith"
   },
   {
      "sequence":"[CLS] the man worked as a lawyer.[SEP]",
      "score":0.01820771023631096,
      "token":3672,
      "token_str":"▁lawyer"
   }
]

>>> unmasker("The woman worked as a [MASK].")

[
   {
      "sequence":"[CLS] the woman worked as a receptionist.[SEP]",
      "score":0.04604868218302727,
      "token":25331,
      "token_str":"▁receptionist"
   },
   {
      "sequence":"[CLS] the woman worked as a janitor.[SEP]",
      "score":0.028220869600772858,
      "token":29477,
      "token_str":"▁janitor"
   },
   {
      "sequence":"[CLS] the woman worked as a paramedic.[SEP]",
      "score":0.0261906236410141,
      "token":23386,
      "token_str":"▁paramedic"
   },
   {
      "sequence":"[CLS] the woman worked as a chauffeur.[SEP]",
      "score":0.024797942489385605,
      "token":28744,
      "token_str":"▁chauffeur"
   },
   {
      "sequence":"[CLS] the woman worked as a waitress.[SEP]",
      "score":0.024124596267938614,
      "token":13678,
      "token_str":"▁waitress"
   }
]

This bias will also affect all fine-tuned versions of this model.

Training Data

The ALBERT model was pretrained on BookCorpus, a dataset consisting of 11,038 unpublished books, and 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 as follows:

• 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:

🔧 Technical Details

The ALBERT model uses a self-supervised learning approach with two main objectives: Masked Language Modeling (MLM) and Sentence Ordering Prediction (SOP). The model shares its layers across the Transformer, which helps reduce the memory footprint.

The masking procedure during training is carefully designed to ensure the model can learn effective representations. The evaluation results show that the ALBERT models perform well on various downstream tasks.

📄 License

This model is licensed under the Apache 2.0 license.

BibTeX entry and citation info

@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}
}