MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers
Paper • 2012.15828 • Published • 1
How to use Unbabel/xlm-roberta-comet-small with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="Unbabel/xlm-roberta-comet-small") # Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Unbabel/xlm-roberta-comet-small")
model = AutoModel.from_pretrained("Unbabel/xlm-roberta-comet-small")YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
mMiniLM-L12xH384 XLM-R model proposed in MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers that we fine-tune using the direct assessment annotations collected in the Workshop on Statistical Machine Translation (WMT) 2015 to 2020.
This model is much more light weight than the traditional XLM-RoBERTa base and large.