Instructions to use llmware/industry-bert-contracts-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llmware/industry-bert-contracts-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="llmware/industry-bert-contracts-v0.1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("llmware/industry-bert-contracts-v0.1") model = AutoModel.from_pretrained("llmware/industry-bert-contracts-v0.1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
pooling method and results
#2
by basilevc - opened
First of all, thanks for the nice work and sharing your model π€
I have a few questions regarding the family of models "industry bert" your team has provided here:
- what kind of pooling method do we use to obtain embeddings from your models? I saw here that you use the CLS (i.e first embedding vector for HF) but would like to be sure
- do you normalize your embeddings ?
- what kind of distance metric is to be used? L2 / dot ?
- have you validated your models on some kind of industry retrieval benchmark ? if so would you be comfortable sharing it ?
also, just FYI (and you probably already know this) to make your model easily loadable via the Sentence BERT framework, you could attach a configuration file such as this model (you obtain these files when you serialize the model via the Sentence Bert Model class): it would make your model seamlessly usable.