
Transformer XL

This model is in maintenance mode only, so we won't accept any new PRs changing its code. This model was deprecated due to security issues linked to pickle.load.
We recommend switching to more recent models for improved security.
In case you would still like to use TransfoXL in your experiments, we recommend using the Hub checkpoint with a specific revision to ensure you are downloading safe files from the Hub.
You will need to set the environment variable TRUST_REMOTE_CODE to True in order to allow the
usage of pickle.load():
thon
import os
from transformers import TransfoXLTokenizer, TransfoXLLMHeadModel
os.environ["TRUST_REMOTE_CODE"] = "True"
checkpoint = 'transfo-xl/transfo-xl-wt103'
revision = '40a186da79458c9f9de846edfaea79c412137f97'
tokenizer = TransfoXLTokenizer.from_pretrained(checkpoint, revision=revision)
model = TransfoXLLMHeadModel.from_pretrained(checkpoint, revision=revision)

If you run into any issues running this model, please reinstall the last version that supported this model: v4.35.0.
You can do so by running the following command: pip install -U transformers==4.35.0.

Overview
The Transformer-XL model was proposed in Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan
Salakhutdinov. It's a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can
reuse previously computed hidden-states to attend to longer context (memory). This model also uses adaptive softmax
inputs and outputs (tied).
The abstract from the paper is the following:
Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the
setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency
beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a
novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the
context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450%
longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+
times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of
bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn
Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably
coherent, novel text articles with thousands of tokens.
This model was contributed by thomwolf. The original code can be found here.
Usage tips

Transformer-XL uses relative sinusoidal positional embeddings. Padding can be done on the left or on the right. The
  original implementation trains on SQuAD with padding on the left, therefore the padding defaults are set to left.
Transformer-XL is one of the few models that has no sequence length limit.
Same as a regular GPT model, but introduces a recurrence mechanism for two consecutive segments (similar to a regular RNNs with two consecutive inputs). In this context, a segment is a number of consecutive tokens (for instance 512) that may span across multiple documents, and segments are fed in order to the model.
Basically, the hidden states of the previous segment are concatenated to the current input to compute the attention scores. This allows the model to pay attention to information that was in the previous segment as well as the current one. By stacking multiple attention layers, the receptive field can be increased to multiple previous segments.
This changes the positional embeddings to positional relative embeddings (as the regular positional embeddings would give the same results in the current input and the current hidden state at a given position) and needs to make some adjustments in the way attention scores are computed.

TransformerXL does not work with torch.nn.DataParallel due to a bug in PyTorch, see issue #36035

Resources

Text classification task guide
Causal language modeling task guide

TransfoXLConfig
[[autodoc]] TransfoXLConfig
TransfoXLTokenizer
[[autodoc]] TransfoXLTokenizer
    - save_vocabulary
TransfoXL specific outputs
[[autodoc]] models.deprecated.transfo_xl.modeling_transfo_xl.TransfoXLModelOutput
[[autodoc]] models.deprecated.transfo_xl.modeling_transfo_xl.TransfoXLLMHeadModelOutput
[[autodoc]] models.deprecated.transfo_xl.modeling_tf_transfo_xl.TFTransfoXLModelOutput
[[autodoc]] models.deprecated.transfo_xl.modeling_tf_transfo_xl.TFTransfoXLLMHeadModelOutput

TransfoXLModel
[[autodoc]] TransfoXLModel
    - forward
TransfoXLLMHeadModel
[[autodoc]] TransfoXLLMHeadModel
    - forward
TransfoXLForSequenceClassification
[[autodoc]] TransfoXLForSequenceClassification
    - forward

TFTransfoXLModel
[[autodoc]] TFTransfoXLModel
    - call
TFTransfoXLLMHeadModel
[[autodoc]] TFTransfoXLLMHeadModel
    - call
TFTransfoXLForSequenceClassification
[[autodoc]] TFTransfoXLForSequenceClassification
    - call

Internal Layers
[[autodoc]] AdaptiveEmbedding
[[autodoc]] TFAdaptiveEmbedding