Proximity Embeddings Returning 4 Digits instead of 8

Recently adapters was updated and now the embeddings returned by specter2 proximity have 4 digits instead of 8 digits of precision.

```shell
pip install adapters
pip install torch
```

This issue can be replicated with
```python
from transformers import AutoTokenizer
from adapters import AutoAdapterModel

# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('allenai/specter2_base')

#load base model
model = AutoAdapterModel.from_pretrained('allenai/specter2_base')

#load the adapter(s) as per the required task, provide an identifier for the adapter in load_as argument and activate it
model.load_adapter("allenai/specter2", source="hf", load_as="specter2", set_active=True)

papers = [{'title': 'BERT', 'abstract': 'We introduce a new language representation model called BERT'},
{'title': 'Attention is all you need', 'abstract': ' The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'}]

# concatenate title and abstract
text_batch = [d['title'] + tokenizer.sep_token + (d.get('abstract') or '') for d in papers]
# preprocess the input
inputs = self.tokenizer(text_batch, padding=True, truncation=True,
return_tensors="pt", return_token_type_ids=False, max_length=512)
output = model(**inputs)
# take the first token in the batch as the embedding
embeddings = output.last_hidden_state[:, 0, :]
print(embeddings)
```

To run
```shell
python3 -m venv .venv
source .venv/bin/activate
pip install adapters
pip install torch
# copy / paste the above python code into test_specter2.py
python test_specter2.py
```

Output
```shell
BertAdapterModel has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
- If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
- If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
- If you are not the owner of the model architecture class, please contact the model code owner to update it.
Fetching 4 files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 70789.94it/s]
/home/sneilan/scratch/specter2/.venv/lib/python3.10/site-packages/adapters/loading.py:165: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
state_dict = torch.load(weights_file, map_location="cpu")
tensor([[-0.2127, 0.5332, -0.6860, ..., -0.4872, 0.1453, -0.7420],
[ 0.2662, 0.5603, 0.0460, ..., -0.1477, -0.1488, 0.0200]],
grad_fn=)
```

There is also new messaging from the adapters library as of 4.50. Adapters is requesting everyone to update their models to inherit from GenerationMixin.