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*This model was released on 2019-09-26 and added to Hugging Face Transformers on 2020-11-16 and contributed by [lysandre](https://huggingface.co/lysandre).*

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        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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# ALBERT

[ALBERT](https://huggingface.co/papers/1909.11942) presents parameter-reduction techniques to enhance BERT by splitting the embedding matrix and using repeating layers. These methods reduce memory usage and training time, enabling better scalability. The model employs a self-supervised loss to improve inter-sentence coherence, achieving state-of-the-art results on GLUE, RACE, and SQuAD benchmarks with fewer parameters than BERT-large.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="fill-mask", model="albert/albert-base-v2", dtype="auto")
pipeline("Plants create [MASK] through a process known as photosynthesis.")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

model = AutoModelForMaskedLM.from_pretrained("albert/albert-base-v2", dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("albert/albert-base-v2")

inputs = tokenizer("Plants create [MASK] through a process known as photosynthesis.", return_tensors="pt")
outputs = model(**inputs)
mask_token_id = tokenizer.mask_token_id
mask_position = (inputs.input_ids == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
predicted_word = tokenizer.decode(outputs.logits[0, mask_position].argmax(dim=-1))
print(f"Predicted word: {predicted_word}")
```

</hfoption>
</hfoptions>

## Usage tips

- ALBERT uses absolute position embeddings. Pad inputs on the right, not the left.

- The embedding size E differs from hidden size H for good reason. Embeddings represent individual tokens (context-independent). Hidden states represent token sequences (context-dependent). This makes H >> E logical. The embedding matrix spans V × E dimensions, where V is vocabulary size. Keeping E < H reduces parameter count.

## AlbertConfig

[[autodoc]] AlbertConfig

## AlbertTokenizer

[[autodoc]] AlbertTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary

## AlbertTokenizerFast

[[autodoc]] AlbertTokenizerFast

## Albert specific outputs

[[autodoc]] models.albert.modeling_albert.AlbertForPreTrainingOutput

## AlbertModel

[[autodoc]] AlbertModel
    - forward

## AlbertForPreTraining

[[autodoc]] AlbertForPreTraining
    - forward

## AlbertForMaskedLM

[[autodoc]] AlbertForMaskedLM
    - forward

## AlbertForSequenceClassification

[[autodoc]] AlbertForSequenceClassification
    - forward

## AlbertForMultipleChoice

[[autodoc]] AlbertForMultipleChoice

## AlbertForTokenClassification

[[autodoc]] AlbertForTokenClassification
    - forward

## AlbertForQuestionAnswering

[[autodoc]] AlbertForQuestionAnswering
    - forward
