Albert Base V1

🚀 ALBERT Base v1

A pre - trained model on English using masked language modeling (MLM) objective, offering bidirectional language representation and useful for various downstream tasks.

🚀 Quick Start

This is a pre - trained model on English language using masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This uncased model 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: Through masked language modeling (MLM), it can learn a bidirectional representation of sentences.
• Sentence Ordering Prediction: Uses a pretraining loss based on predicting the ordering of two consecutive text segments.
• Shared Layers: Shares layers across its Transformer, resulting in a small memory footprint.

📦 Installation

No specific installation steps are provided in the original README. However, you can use this model through the transformers library in Python. You can install transformers using 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-base-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-base-v1')
model = AlbertModel.from_pretrained("albert-base-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-base-v1')
model = TFAlbertModel.from_pretrained("albert-base-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): Randomly masks 15% of the words in the input sentence, then predicts the masked words. This allows the model to learn a bidirectional representation of the sentence.
• Sentence Ordering Prediction (SOP): Uses a pretraining loss based on predicting the ordering of two consecutive segments of text.

ALBERT shares its layers across its Transformer, so all layers have the same weights. Using repeating layers results in a small memory footprint, but the computational cost is similar to a BERT - like architecture with the same number of hidden layers.

This is the first version of the base 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-base-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 with 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:

🔧 Technical Details

The model's unique architecture of sharing layers across the Transformer allows it to have a small memory footprint. However, it still needs to iterate through the same number of (repeating) layers, so the computational cost is similar to a BERT - like architecture with the same number of hidden layers.

📄 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}
}