nomic2vec.py: Work around missing NomicBertModel::*_input_embeddings

[vsrinivas]

NomicBertModel is missing get_input_embeddings/set_input_embeddings, which are required by model2vec's distilation pipeline. Patch in the two methods.

Before:
...
HuggingFace tokenizer defines a pad_token, but the Skeletoken model does not. Setting it to ''.

Full traceback of the error:
Traceback (most recent call last):
File "/home/vsrinivas/WORK/semcode/./scripts/nomic2vec.py", line 501, in main
m2v = distill_from_model(
model=model,
...<3 lines>...
device=args.device
)
File "/home/vsrinivas/semcode-vectors2/lib/python3.13/site-packages/model2vec/distill/distillation.py", line 107, in distill_from_model
model = reshape_embeddings(model, original_tokenizer_model)
File "/home/vsrinivas/semcode-vectors2/lib/python3.13/site-packages/skeletoken/external/transformers.py", line 58, in reshape_embeddings
embedding = model.get_input_embeddings()
File "/home/vsrinivas/semcode-vectors2/lib/python3.13/site-packages/transformers/modeling_utils.py", line 1036, in get_input_embeddings
raise NotImplementedError(
f"get_input_embeddings not auto‑handled for {self.class.name}; please override in the subclass."
)
NotImplementedError: get_input_embeddings not auto‑handled for NomicBertModel; please override in the subclass.

After:
HuggingFace tokenizer defines a pad_token, but the Skeletoken model does not. Setting it to ''. Encoding tokens: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 249999/249999 [2:12:08<00:00, 31.53 tokens/s] ✓ Saved Model2Vec static model to /home/vsrinivas/WORK/semcode/nomic_v2_m2v Fixing tokenizer configuration for semcode compatibility...
Adding [UNK] token to vocabulary
Set unk_id to 1 for [UNK] token
✓ Updated tokenizer configuration
✓ Updated tokenizer_config.json
⚠ Verification warning: Number of tokens (250000) does not match number of vectors (249999). Please provide a token mapping or ensure the number of tokens matches the number of vectors.
The model was saved successfully but may need additional configuration for some tools

All done. The static model is ready for high‑throughput CPU embedding.

[meta-cla]

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[masoncl]

There's no need to sign the CLA, just make sure your commits have Signed-off-by: tags. I'll take a look, thanks for sending this in

[vsrinivas]

Thanks!

By the way, one thing I was curious about in code close to this -- nomic2vec.py can fall back to model2vec.distill_from_sentence_transformer --- I couldn't find distill_from_sentence_transformer anywhere, even in the history of model2vec. How were you using that fallback?

[vsrinivas]

There's no need to sign the CLA, just make sure your commits have Signed-off-by: tags. I'll take a look, thanks for sending this in

Updated w/ an Assisted-by footer; just found out about them -- this was assisted by Claude Code running against a local gemma4 model.