transformers/model_cards/mrm8488/longformer-base-4096-finetuned-squadv2

language	datasets
english	squad_v2

Longformer-base-4096 fine-tuned on SQuAD v2

Longformer-base-4096 model fine-tuned on SQuAD v2 for Q&A downstream task.

Longformer-base-4096

Longformer is a transformer model for long documents.

longformer-base-4096 is a BERT-like model started from the RoBERTa checkpoint and pretrained for MLM on long documents. It supports sequences of length up to 4,096.

Longformer uses a combination of a sliding window (local) attention and global attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations.

Details of the downstream task (Q&A) - Dataset 📚 🧐 ❓

Dataset ID: squad_v2 from HugginFace/NLP

Dataset	Split	# samples
squad_v2	train	130319
squad_v2	valid	11873

How to load it from nlp

train_dataset  = nlp.load_dataset('squad_v2', split=nlp.Split.TRAIN)
valid_dataset = nlp.load_dataset('squad_v2', split=nlp.Split.VALIDATION)

Check out more about this dataset and others in NLP Viewer

Model fine-tuning 🏋️‍

The training script is a slightly modified version of this one

Model in Action 🚀

import torch
from transformers import AutoTokenizer, AutoModelForQuestionAnswering

tokenizer = AutoTokenizer.from_pretrained("mrm8488/longformer-base-4096-finetuned-squadv2")
model = AutoModelForQuestionAnswering.from_pretrained("mrm8488/longformer-base-4096-finetuned-squadv2")

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
attention_mask = encoding["attention_mask"]

start_scores, end_scores = model(input_ids, attention_mask=attention_mask)
all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())

answer_tokens = all_tokens[torch.argmax(start_scores) :torch.argmax(end_scores)+1]
answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))

# output => democratized NLP

If given the same context we ask something that is not there, the output for no answer will be <s>

Created by Manuel Romero/@mrm8488 | LinkedIn

Made with ♥ in Spain