XLM-R Longformer Model
This is an XLM-RoBERTa longformer model that was pre-trained from the XLM-RoBERTa checkpoint using the Longformer pre-training scheme on the English WikiText-103 corpus.
This model is identical to markussagen's xlm-r longformer model, the difference being that the weights have been transferred to a Longformer model, in order to enable loading with
AutoModel.from_pretrained() without external dependencies.
Note that this model requires a considerable amount of memory to run. The heatmap below should give a relative idea of the amount of memory needed at inference for a target batch and sequence length. N.B. data for this plot was generated by running on a single a100 GPU with 40gb of memory.
How to Use
The model can be used as expected to fine-tune on a downstream task.
For instance for QA.
import torch from transformers import AutoModel, AutoTokenizer MAX_SEQUENCE_LENGTH = 4096 MODEL_NAME_OR_PATH = "AshtonIsNotHere/xlm-roberta-long-base-4096" tokenizer = AutoTokenizer.from_pretrained( MODEL_NAME_OR_PATH, max_length=MAX_SEQUENCE_LENGTH, padding="max_length", truncation=True, ) model = AutoModelForQuestionAnswering.from_pretrained( MODEL_NAME_OR_PATH, max_length=MAX_SEQUENCE_LENGTH, )
- Downloads last month