Feature Extraction
Transformers
PyTorch
multilingual
bert
STILT
retraining
multi-task learning
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("robvanderg/Sem-mmmBERT")
model = AutoModel.from_pretrained("robvanderg/Sem-mmmBERT")Quick Links
YAML Metadata Error:"datasets[0]" with value "SemEval 2022" is not valid. If possible, use a dataset id from https://hf.co/datasets.
Sem-mmmBERT
This is the SemEval MaChAmp Multitask Multilingual BERT model. This model is retrained from mBERT (https://huggingface.co/bert-base-multilingual-cased).
The retraining is done based on all SemEval 2022 tasks that are text based, and have annotation on the word, sentence or paragraph level. The retraining is done with MaChAmp (https://machamp-nlp.github.io/), a toolkit focusing on multi-task learning for NLP. More information can be found in the paper (which should be released when the SemEval proceedings are online).
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="robvanderg/Sem-mmmBERT")