# LONGFORMER-BASE-4096 fine-tuned on SQuAD v1

Longformer model created by Iz Beltagy, Matthew E. Peters, Arman Coha from AllenAI. As the paper explains it

Longformer is a BERT-like model for long documents.

The pre-trained model can handle sequences with upto 4096 tokens.

## Model Training

This model was trained on google colab v100 GPU. You can find the fine-tuning colab here .

Few things to keep in mind while training longformer for QA task, by default longformer uses sliding-window local attention on all tokens. But For QA, all question tokens should have global attention. For more details on this please refer the paper. The LongformerForQuestionAnswering model automatically does that for you. To allow it to do that

1. The input sequence must have three sep tokens, i.e the sequence should be encoded like this  <s> question</s></s> context</s>. If you encode the question and answer as a input pair, then the tokenizer already takes care of that, you shouldn't worry about it.
2. input_ids should always be a batch of examples.

## Results

Metric # Value
Exact Match 85.1466
F1 91.5415

## Model in Action 🚀

import torch

text = "Huggingface has democratized NLP. Huge thanks to Huggingface for this."
question = "What has Huggingface done ?"
encoding = tokenizer(question, text, return_tensors="pt")
input_ids = encoding["input_ids"]

# default is local attention everywhere
# the forward method will automatically set global attention on question tokens

all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())

# output => democratized NLP


The LongformerForQuestionAnswering isn't yet supported in pipeline . I'll update this card once the support has been added.

Created with ❤️ by Suraj Patil

New

Select AutoNLP in the “Train” menu to fine-tune this model automatically.