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Create ALBERT XXLarge v2 model card

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  ---
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  tags:
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  - exbert
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-
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  license: apache-2.0
 
 
 
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  ---
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  <a href="https://huggingface.co/exbert/?model=albert-xxlarge-v2">
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  <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
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  </a>
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  ---
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  tags:
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  - exbert
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+ language: en
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  license: apache-2.0
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+ datasets:
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+ - bookcorpus
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+ - wikipedia
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  ---
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+ # ALBERT XXLarge v2
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+
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+ Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
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+ [this paper](https://arxiv.org/abs/1909.11942) and first released in
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+ [this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a difference
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+ between english and English.
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+
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+ Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by
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+ the Hugging Face team.
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+
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+ ## Model description
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+
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+ ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
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+ was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
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+ publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
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+ was pretrained with two objectives:
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+
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+ - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
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+ the entire masked sentence through the model and has to predict the masked words. This is different from traditional
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+ recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
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+ GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
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+ sentence.
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+ - Sentence Ordering Prediction (SOP): ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text.
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+
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+ This way, the model learns an inner representation of the English language that can then be used to extract features
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+ useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
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+ classifier using the features produced by the ALBERT model as inputs.
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+
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+ 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.
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+
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+ This is the second version of the xxlarge 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.
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+
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+ This model has the following configuration:
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+
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+ - 12 repeating layers
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+ - 128 embedding dimension
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+ - 4096 hidden dimension
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+ - 64 attention heads
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+ - 223M parameters
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+
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+ ## Intended uses & limitations
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+
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+ You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
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+ be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=albert) to look for
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+ fine-tuned versions on a task that interests you.
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+
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+ Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
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+ to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
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+ generation you should look at model like GPT2.
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+
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+ ### How to use
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+
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+ You can use this model directly with a pipeline for masked language modeling:
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+
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+ ```python
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+ >>> from transformers import pipeline
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+ >>> unmasker = pipeline('fill-mask', model='albert-xxlarge-v2')
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+ >>> unmasker("Hello I'm a [MASK] model.")
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+ [
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+ {
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+ "sequence":"[CLS] hello i'm a modeling model.[SEP]",
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+ "score":0.05816134437918663,
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+ "token":12807,
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+ "token_str":"▁modeling"
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+ },
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+ {
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+ "sequence":"[CLS] hello i'm a modelling model.[SEP]",
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+ "score":0.03748830780386925,
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+ "token":23089,
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+ "token_str":"▁modelling"
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+ },
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+ {
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+ "sequence":"[CLS] hello i'm a model model.[SEP]",
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+ "score":0.033725276589393616,
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+ "token":1061,
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+ "token_str":"▁model"
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+ },
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+ {
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+ "sequence":"[CLS] hello i'm a runway model.[SEP]",
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+ "score":0.017313428223133087,
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+ "token":8014,
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+ "token_str":"▁runway"
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+ },
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+ {
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+ "sequence":"[CLS] hello i'm a lingerie model.[SEP]",
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+ "score":0.014405295252799988,
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+ "token":29104,
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+ "token_str":"▁lingerie"
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+ }
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+ ]
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+ ```
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+
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+ Here is how to use this model to get the features of a given text in PyTorch:
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+
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+ ```python
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+ from transformers import AlbertTokenizer, AlbertModel
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+ tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v2')
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+ model = AlbertModel.from_pretrained("albert-xxlarge-v2")
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+ text = "Replace me by any text you'd like."
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+ encoded_input = tokenizer(text, return_tensors='pt')
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+ output = model(**encoded_input)
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+ ```
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+
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+ and in TensorFlow:
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+
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+ ```python
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+ from transformers import AlbertTokenizer, TFAlbertModel
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+ tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v2')
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+ model = TFAlbertModel.from_pretrained("albert-xxlarge-v2")
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+ text = "Replace me by any text you'd like."
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+ encoded_input = tokenizer(text, return_tensors='tf')
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+ output = model(encoded_input)
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+ ```
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+
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+ ### Limitations and bias
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+
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+ Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
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+ predictions:
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+
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+ ```python
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+ >>> from transformers import pipeline
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+ >>> unmasker = pipeline('fill-mask', model='albert-xxlarge-v2')
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+ >>> unmasker("The man worked as a [MASK].")
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+
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+ [
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+ {
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+ "sequence":"[CLS] the man worked as a chauffeur.[SEP]",
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+ "score":0.029577180743217468,
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+ "token":28744,
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+ "token_str":"▁chauffeur"
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+ },
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+ {
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+ "sequence":"[CLS] the man worked as a janitor.[SEP]",
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+ "score":0.028865724802017212,
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+ "token":29477,
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+ "token_str":"▁janitor"
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+ },
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+ {
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+ "sequence":"[CLS] the man worked as a shoemaker.[SEP]",
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+ "score":0.02581118606030941,
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+ "token":29024,
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+ "token_str":"▁shoemaker"
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+ },
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+ {
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+ "sequence":"[CLS] the man worked as a blacksmith.[SEP]",
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+ "score":0.01849772222340107,
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+ "token":21238,
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+ "token_str":"▁blacksmith"
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+ },
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+ {
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+ "sequence":"[CLS] the man worked as a lawyer.[SEP]",
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+ "score":0.01820771023631096,
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+ "token":3672,
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+ "token_str":"▁lawyer"
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+ }
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+ ]
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+
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+ >>> unmasker("The woman worked as a [MASK].")
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+
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+ [
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+ {
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+ "sequence":"[CLS] the woman worked as a receptionist.[SEP]",
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+ "score":0.04604868218302727,
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+ "token":25331,
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+ "token_str":"▁receptionist"
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+ },
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+ {
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+ "sequence":"[CLS] the woman worked as a janitor.[SEP]",
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+ "score":0.028220869600772858,
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+ "token":29477,
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+ "token_str":"▁janitor"
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+ },
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+ {
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+ "sequence":"[CLS] the woman worked as a paramedic.[SEP]",
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+ "score":0.0261906236410141,
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+ "token":23386,
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+ "token_str":"▁paramedic"
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+ },
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+ {
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+ "sequence":"[CLS] the woman worked as a chauffeur.[SEP]",
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+ "score":0.024797942489385605,
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+ "token":28744,
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+ "token_str":"▁chauffeur"
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+ },
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+ {
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+ "sequence":"[CLS] the woman worked as a waitress.[SEP]",
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+ "score":0.024124596267938614,
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+ "token":13678,
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+ "token_str":"▁waitress"
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+ }
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+ ]
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+ ```
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+
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+ This bias will also affect all fine-tuned versions of this model.
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+
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+ ## Training data
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+
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+ The ALBERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
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+ unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
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+ headers).
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+
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+ ## Training procedure
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+
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+ ### Preprocessing
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+
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+ The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 30,000. The inputs of the model are
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+ then of the form:
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+
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+ ```
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+ [CLS] Sentence A [SEP] Sentence B [SEP]
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+ ```
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+
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+ ### Training
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+
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+ The ALBERT procedure follows the BERT setup.
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+
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+ The details of the masking procedure for each sentence are the following:
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+ - 15% of the tokens are masked.
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+ - In 80% of the cases, the masked tokens are replaced by `[MASK]`.
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+ - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
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+ - In the 10% remaining cases, the masked tokens are left as is.
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+
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+ ## Evaluation results
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+
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+ When fine-tuned on downstream tasks, the ALBERT models achieve the following results:
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+
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+ | | Average | SQuAD1.1 | SQuAD2.0 | MNLI | SST-2 | RACE |
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+ |----------------|----------|----------|----------|----------|----------|----------|
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+ |V2 |
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+ |ALBERT-base |82.3 |90.2/83.2 |82.1/79.3 |84.6 |92.9 |66.8 |
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+ |ALBERT-large |85.7 |91.8/85.2 |84.9/81.8 |86.5 |94.9 |75.2 |
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+ |ALBERT-xlarge |87.9 |92.9/86.4 |87.9/84.1 |87.9 |95.4 |80.7 |
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+ |ALBERT-xxlarge |90.9 |94.6/89.1 |89.8/86.9 |90.6 |96.8 |86.8 |
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+ |V1 |
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+ |ALBERT-base |80.1 |89.3/82.3 | 80.0/77.1|81.6 |90.3 | 64.0 |
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+ |ALBERT-large |82.4 |90.6/83.9 | 82.3/79.4|83.5 |91.7 | 68.5 |
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+ |ALBERT-xlarge |85.5 |92.5/86.1 | 86.1/83.1|86.4 |92.4 | 74.8 |
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+ |ALBERT-xxlarge |91.0 |94.8/89.3 | 90.2/87.4|90.8 |96.9 | 86.5 |
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+
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+
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+ ### BibTeX entry and citation info
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+
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+ ```bibtex
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+ @article{DBLP:journals/corr/abs-1909-11942,
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+ author = {Zhenzhong Lan and
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+ Mingda Chen and
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+ Sebastian Goodman and
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+ Kevin Gimpel and
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+ Piyush Sharma and
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+ Radu Soricut},
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+ title = {{ALBERT:} {A} Lite {BERT} for Self-supervised Learning of Language
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+ Representations},
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+ journal = {CoRR},
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+ volume = {abs/1909.11942},
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+ year = {2019},
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+ url = {http://arxiv.org/abs/1909.11942},
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+ archivePrefix = {arXiv},
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+ eprint = {1909.11942},
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+ timestamp = {Fri, 27 Sep 2019 13:04:21 +0200},
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+ biburl = {https://dblp.org/rec/journals/corr/abs-1909-11942.bib},
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+ bibsource = {dblp computer science bibliography, https://dblp.org}
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+ }
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+ ```
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+
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  <a href="https://huggingface.co/exbert/?model=albert-xxlarge-v2">
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  <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
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  </a>