If spaCy’s NER isn’t picking up what you need, you’ll probably need to look into creating your own annotations and fine tuning a model or training a custom model. It isn’t too hard using BIO/BILOU tags. Things like “raw materials” and particularly niche models and brands are unlikely to be picked up by off the shelf solutions.

One option is Stanford NER, which is a named entity recognition tool developed by Stanford University. It uses a CRF (conditional random field) model trained on a large dataset of named entities, and it's relatively fast and accurate. Stanford NER also has pre-trained models available for various languages, so you could use one of these models or train your own model on a custom dataset.
Another option is spaCy, which is a popular natural language processing (NLP) library for Python. spaCy includes a named entity recognition component that uses a convolutional neural network (CNN) to identify named entities in text. It's generally quite accurate and fast, and it has pre-trained models available for various languages. spaCy also provides tools for training custom models on your own dataset, if you have specific named entities that you'd like the model to recognize.
Finally, you might also consider using the Google Cloud Natural Language API, which is a cloud-based NER service provided by Google. The API uses a machine learning model to identify named entities in text, and it's generally quite accurate and fast. It has pre-trained models available for various languages, and it provides tools for training custom models on your own dataset.

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This was generated by chatgpt, 100%.

Are you using the "en_core_web_trf" model in Spacy which is based on the roberta-base transformer model?

If that model is still not accurate enough, you may need to look into using the Hugging Face transformers library and try some more recent transformer models, e.g. deberta