Feature Extraction
Transformers
Safetensors
English
dual_task_tld
tld
embeddings
domains
multi-task-learning
bert
Eval Results (legacy)
Instructions to use humbleworth/tld-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use humbleworth/tld-embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="humbleworth/tld-embedding")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("humbleworth/tld-embedding", dtype="auto") - Notebooks
- Google Colab
- Kaggle
missing files
#1
by igprod - opened
Hi HumbleWorth team,
Thanks a lot for releasing the TLD embedding model and the domain-MLM checkpoint.
We’re integrating your models in a domain appraisal pipeline
(Kaggle/Colab + GPU), and we’d like to load humbleworth/tld-embedding as a
drop-in SentenceTransformers model when possible.
Currently, the repo provides model.safetensors and tokenizer files, but it lacksmodules.json and config_sentence_transformers.json (404 on the Hub).
Also, the config has model_type: "dual_task_tld", which suggests a custom
architecture. Could you please provide one of the following?
- A SentenceTransformers export (i.e.,
modules.json+config_sentence_transformers.json)
so we can load viaSentenceTransformer(<local_dir>), or - The minimal custom class / loader code for
"dual_task_tld"in Transformers,
and the expected MASK token / special tokens behavior to replicate your inference.
Repo links:
- TLD embeddings: https://huggingface.co/humbleworth/tld-embedding
Many thanks for your great work!