Working with the new `._.trf_data` object (3.7+) about spacy HOT 7 CLOSED

So to get back to the original question, doc._.trf_data.last_hidden_layer_state is a Ragged object where you can use the spacy token index to access the tensor data for that token, without having to do any additional alignment on your side.

The data for each token is also a Ragged object:

import spacy
nlp = spacy.load("en_core_web_trf")
doc = nlp("DocTransformerOutput.last_hidden_layer_state is a Ragged object")

# for the tensors corresponding to "DocTransformerOutput.last_hidden_layer_state"
# (token index 0), you can access doc._.trf_data.last_hidden_layer_state[0].data
assert doc._.trf_data.last_hidden_layer_state[0].data.shape == (12, 768)

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Great! That answers my question, and that's a very intuitive way to access the tensor by token index.

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spaCy 3.7 switched to the Curated Transformers library. The DocTransformerOutput class is documented here:

https://spacy.io/api/curatedtransformer#doctransformeroutput

The last_hidden_layer_state property provides the per-token hidden representations for every document.

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Ah, this probably should have been documented better as part of the release.

At first glance, the DocTransformerOutput seems to contain quite a bit less information than the spacy-transformers ModelOutput, in particular I don't see enough info to align the tensors with anything in the doc, but maybe I am mistaken?

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Ah, the Ragged lengths align to spacy tokens? (I admit that I hadn't looked too closely at the details here before, which is part of why this was missed in the release notes.)

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Ah, the Ragged lengths align to spacy tokens? (I admit that I hadn't looked too closely at the details here before, which is part of why this was missed in the release notes.)

Yeah, they do. spacy-curated-transformers applies piecing to tokens, so it doesn't have to do the same alignment as spacy-transformers (modulo whitespace tokens).

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