license: apache-2.0
language:
- en
- fr
- es
- multilingual
widget:
- text: >-
Critical levels of out of <mask> <mask> were reported, with 72% of
respondents pointing to moderate to high numbers of primary school age
children not accessing education.
HumBert
HumBert is a XLM-Roberta model trained on humanitarian texts - approximately 50 million textual examples (roughly 2 billion tokens) from public humanitarian reports, law cases and news articles. Data were collected from three main sources: Reliefweb, UNHCR Refworld and Europe Media Monitor News Brief. Although XLM-Roberta was trained on 100 different languages, this fine-tuning was performed on three languages, English, French and Spanish, due to the impossibility of finding a good amount of such kind of humanitarian data in other languages.
Intended uses & limitations
To the best of our knowledge, HumBert is the first language model adapted on humanitarian topics, which often use a very specific language, making adaptation to downstream tasks (such as dister responses text classification) more effective. This model is primarily aimed at being fine-tuned on tasks such as sequence classification or token classification.
Benchmarks
Soon...
Usage
Here is how to use this model to get the features of a given text in PyTorch:
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained('nlp-thedeep/humbert')
model = AutoModelForMaskedLM.from_pretrained("nlp-thedeep/humbert")
# prepare input
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
# forward pass
output = model(**encoded_input)